Post on 07-Sep-2020
WWDC17
copy 2017 Apple Inc All rights reserved Redistribution or public display not permitted without written permission from Apple
Vivek Kumar Rangarajan Sridhar Software Engineering Manager Doug Davidson Senior Engineer
bullNatural Language Processing bulland your Apps bull Session 208
App Frameworks
Goal
Goal
Goal
Natural language input typed text
recognized handwriting
transcribed speech
Goal
Natural language output typed text
recognized handwriting
transcribed speech
Goal
NLP
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Goal
Goal
Goal
Natural language input typed text
recognized handwriting
transcribed speech
Goal
Natural language output typed text
recognized handwriting
transcribed speech
Goal
NLP
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Goal
Goal
Natural language input typed text
recognized handwriting
transcribed speech
Goal
Natural language output typed text
recognized handwriting
transcribed speech
Goal
NLP
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Goal
Natural language input typed text
recognized handwriting
transcribed speech
Goal
Natural language output typed text
recognized handwriting
transcribed speech
Goal
NLP
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Goal
Natural language output typed text
recognized handwriting
transcribed speech
Goal
NLP
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Goal
NLP
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Processing
Natural language text typed
recognized handwriting
transcribed speech
Extract information
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Confer intelligence
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Welcome to our talk on Natural Language ProcessingBienvenidos a nuestra plaacutetica sobre Procesamiento de Lenguaje Natural
Willkommen zu unserem Vortrag uumlber Verarbeitung natuumlrlicher Sprache欢迎参加我们关于自然语言处理理的讲座
हमारी पराकितक भाषा परससकरण भाषण म आपका सवागत ह
Natural language text typed
recognized handwriting
transcribed speech
Detect language
Language identification
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Tokenize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
星期二蒂姆middot库克先生主持了了苹果公司的财报会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
星期二蒂姆middot库克 先生 主持 了了 苹果公司 的 财报 会议
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Tokenize text
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday Noun PUNCTNounNNP NNP PPNoun NounPPNounVerb PP DT Noun
Assign parts of speech
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hours
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple The stock was up 3 after hoursVerb NounVerb
preside hourbe
Lemmatize text
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Mr Tim Cook presided over the earnings report of Apple Inc on Tuesday
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Mr Tim Cook presided over the earnings report of Apple Inc on TuesdayPER ORG
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Extract entities
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Linguistics
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Natural Language Processing
Linguistics Machine learning
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Named entity recognition
Confer intelligence
Natural language text typed
recognized handwriting
transcribed speech
NLP APIs
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Enough Tell me how to use them
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NLP APIs
NSLinguisticTagger
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Class in foundation
Segment and tag text
Linguistic tasks tagSchemes
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTaggerNEW
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
NEW
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
Units and schemes
NEW
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
Tagging units
Units and schemes
NEW
class func availableTagSchemes (for unit NSLinguisticTaggerUnit language String) -gt [NSLinguisticTagScheme]
public enum NSLinguisticTaggerUnit Int case word case sentence case paragraph case document
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTaggerNEW
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
dominantLanguage
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
NEW
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
NSLinguisticTagger
dominantLanguage
Swift 4 named types for tags and tagSchemes
Improved performance
Higher accuracy
Additional language support
NEW
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow and Whisk
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow and Whisk
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow and Whisk
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Tag photos with descriptions
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Tag photos with descriptions
We hiked to the top and then ran down Lizzie won
Kids partied with all the goodieshellip
Die Kleinen haben friedlich zusammen gespielt
July 4th 2016hellipsummer parties at the
Holdenrsquos begin
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Search for pictures
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Search for pictures
Hike
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Search for pictures
Hike No results
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Improve search experience using NLP
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Improve search experience using NLP
Hike
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow Improve search experience using NLP
We hiked to the top and then ran down Lizzie won
Great hikes make great pics
The hiking boysHolden family loves hiking and also posing for pictures
Hike
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Winnow
NLP
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Part of speech
Lemmatization
Winnow
NLP
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Identification
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [language] options 0)
taggerstring = Die Kleinen haben friedlich zusammen gespielt
let language = taggerdominantLanguage
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Tokenization
import Foundation
let tagger = NSLinguisticTagger(tagSchemes [tokenType] options 0)
let text = NSLinguisticTagger provides text processing APIsn NSLinguisticTagger 是苹果的文字处理理平台
taggerstring = text let range = NSRange(location 0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme tokenType options options) tag tokenRange stop in let token = (text as NSString)substring(with tokenRange) Do something with each token
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Lemmatization
import Foundation let tagger = NSLinguisticTagger(tagSchemes[lemma] options 0) let text = Great hikes make great pics Wonderful afternoon in Marin County
taggerstring = text let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace]
taggerenumerateTags(in range unit word scheme lemma options options) tag tokenRange stop in if let lemma = tagrawValue Do something with each lemma
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
bullDemo
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk Collate social media feeds
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk Collate social media feeds
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk Organize feeds by People Organization and Location using NLP
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk Organize feeds by People Organization and Location using NLP
Tim Cook Apple Stevie Wonder Cupertino Angela Ahrendts Bruce Wayne Thomas Vintberg
Pharrell NYU
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Whisk
NLP
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language identification
TokenizationWord Sentence Paragraph
Named entity recognition
Whisk
NLP
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Named Entity Recognition
import Foundation let tagger = NSLinguisticTagger(tagSchemes [nameType] options 0)
let text = Tim Cook is the CEO of Apple Inc which is located in Cupertino California
taggerstring = text
let range = NSRange(location0 length textutf16count) let options NSLinguisticTaggerOptions = [omitPunctuation omitWhitespace joinNames] let tags [NSLinguisticTag] = [personalName placeName organizationName] taggerenumerateTags(in range unit word scheme nameType options options) tag tokenRange stop in if let tag = tag tagscontains(tag) let name = (text as NSString)substring(with tokenRange)
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
bullDemo
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Why should you use the NLP APIs
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Homogeneous Text Processing
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Homogeneous Text Processing
NLP APIs
Natural language text typed
recognized handwriting
transcribed speech
Consistent text processing
Consistent user experience
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Privacy
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Privacy
On-device machine learning
User data stays on-device
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Chinese tokenization is 30 faster on iOS
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
Highly optimized on-device
Multi-threaded
Existing clientsmdashsignificant speedup
Named Entity Recognition 80 faster on iOS
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Performance
50000 tokenssec
80000 tokenssec
Part of Speech Tagging
1 thread
40000 tokenssec
65000 tokenssec
Named Entity Recognition
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language identification 29 scripts 52 languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Language Support
Language identification 29 scripts 52 languages
Tokenization All iOSmacOS system languages
LemmatizationEnglish French Italian
German Spanish
Portuguese Russian Turkish
Part of speech
Named entity recognition
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Accuracy
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Accuracy
0
20
40
60
80
100
Part of speech
Named entity recognition
English Spanish
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Debugging Hints
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Debugging Hints
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Debugging Hints
Downloaded over-the-air
Tags are NSLinguisticTagOtherWord bull Model not downloaded
Explicitly set language if known
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Summary
NLP APIsmdashNSLinguisticTagger bull Support for new units bull Faster bull Higher accuracy bull More languages
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
More Informationhttpsdeveloperapplecomwwdc17208
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Related Sessions
Whats New in Cocoa Touch WWDC 2017
Introducing Core ML WWDC 2017
Vision Framework Building on Core ML Hall 2 Wednesday 310PM
Core ML in Depth Hall 3 Thursday 900 AM
Accelerate and Sparse Solvers Grand Ballroom A Thursday 1000 AM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM
Labs
Core ML amp Natural Language Processing Lab Technology Lab D Thu 1100AM-330PM
Vision Lab Technology Lab A Fri 150PM-400PM
Core ML amp Natural Language Processing Lab Technology Lab D Fri 150PM-400PM
Cocoa Lab Technology Lab B Fri 150PM-320PM