Ling 570: Day 8 Classification, Mallet 1. Roadmap Open questions? Quick review of classification ...

1

Ling 570: Day 8Classification, Mallet

2

Roadmap

Open questions? Quick review of classification Feature templates

3

Classification Problem Steps

Input processing: Split data into training/dev/test Convert data into a feature representation (aka

Attribute Value Matrix) Training Testing Evaluation

4

Feature templates

Problem: predict the POS tag distribution of an unknown word

Input: “unfrobulate” Input: “turduckenly”

word w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly

unfrobulate ate te 1 0 1 0

turduckenly nly ly 0 1 0 1

5

Feature templates



Features might include:




6

Feature templates



Features might include: Last three characters are “ate” Last two characters are “ly”




7

Feature templates

Problem: predict the POS tag distribution of an unknown word Input: “unfrobulate” Input: “turduckenly”


Feature templates generate features given an input Template : Last three characters == XXX.




8

Feature templates

Problem: predict the POS tag distribution of an unknown word Input: “unfrobulate” Input: “turduckenly”


Feature templates generate features given an input Template : Last three characters == XXX. Plug in XXX to get a binary valued feature. Templates generate many featuresword w[-3..-1] w[-2..-1] w[-3..-1]==ate w[-3..-1]==nly w[-2,-1]=te w[-2,-1]=ly



9

Machine learning

10

Classifiers

Wide variety Differ on several dimensions

Supervision

Learning Function

Input Features

11

Supervision in Classifiers Supervised:

True label/class of each training instance is provided to the learner at training time

Naïve Bayes, MaxEnt, Decision Trees, Neural nets, etc

12



Naïve Bayes, MaxEnt, Decision Trees, Neural nets, etc Unsupervised:

No true labels are provided for examples during training Clustering: k-means; Min-cut algorithms

13



Naïve Bayes, MaxEnt, Decision Trees, Neural nets, etc Unsupervised:

No true labels are provided for examples during training Clustering: k-means; Min-cut algorithms

Semi-supervised: (bootstrapping) True labels are provided for only a subset of examples Co-training, semi-supervised SVM/CRF, etc

14

Inductive Bias

What form of function is learned? Function that separates members of different classes

Linear separator Higher order functions Vornoi diagrams, etc

15

Inductive Bias

What form of function is learned? Function that separates members of different classes

Linear separator Higher order functions Vornoi diagrams, etc

Graphically, decision boundary

+ + + - - -

16

Machine Learning Functions

Problem: Can the representation effectively model the class to be learned?

17



Motivates selection of learning algorithm

++ + + + +

- - - - - - - - -

18




++ + + + +

- - - - - - - - -

For this function,Linear discriminant is GREAT!

19




++ + + + +

- - - - - - - - -

For this function,Linear discriminant is GREAT!Rectangular boundaries (e.g. ID trees)

TERRIBLE!

20




++ + + + +

- - - - - - - - -

For this function,Linear discriminant is GREAT!Rectangular boundaries (e.g. ID trees)

TERRIBLE!

Pick the right representation!

21

Machine Learning Features

Inputs: E.g.words, acoustic measurements, parts-of-speech,

syntactic structures, semantic classes, ..

Vectors of features: E.g. word: letters

‘cat’: L1=c; L2 = a; L3 = t Parts of syntax trees?

22

Machine Learning Features

Questions: Which features and values should be used? How should they relate to each other?

Issue 1: What values should they take? Binary features – don’t do anything! Real valued features *may* need to be normalized

Can force the values to have 0 mean and unit variance Compute the mean and variance on the training set for real valued

feature Replace original value with

Can also bin them or binarize them – often this works better Issue 2: Which ones are important?

Feature selection is sometimes important Current approach

23

Machine Learning Toolkits

Many learners, many tools/implementations

24



Some broad tool sets weka

Java, lots of classifiers, pedagogically oriented

25



Some broad tool sets weka

Java, lots of classifiers, pedagogically oriented

mallet Java, classifiers, sequence learners More heavy duty

26

Mallet: intro and data prep

27

Mallet

Machine learning toolkit Developed at UMass Amherst by Andrew McCallum

28

Mallet


Java implementation, open source

29

Mallet



Large collection of machine learning algorithms Targeted to language processing Naïve Bayes, MaxEnt, Decision Trees, Winnow, Boosting Also, clustering, topic models, sequence learners

30

Mallet



Large collection of machine learning algorithms Targeted to language processing Naïve Bayes, MaxEnt, Decision Trees, Winnow, Boosting Also, clustering, topic models, sequence learners

Widely used, but Research software: some bugs/gaps; odd documentation

31

Installation

Installed on patas /NLP_TOOLS/tool_sets/mallet/latest/

Directories: bin/: script files src/: java source code class/: java classes lib/: jar files sample-data/: wikipedia docs for languages id, etc

32

Environment Should be set up on patas

$PATH should include /NLP_TOOLS/tool_sets/mallet/latest/bin

$CLASSPATH should include /NLP_TOOLS/tool_sets/mallet/latest/lib/mallet-deps.jar;

/NLP_TOOLS/tool_sets/mallet/latest/lib/mallet.jar

Check: which text2vectors

/NLP_TOOLS/tool_sets/mallet/latest/bin

33

Mallet Commands

Mallet command types: Data preparation Data/model inspection Training Classification

34

Mallet Commands

Mallet command types: Data preparation Data/model inspection Training Classification

Command line scripts Shell scripts

Set up java environment Invoke java programs

--help lists command line parameters for scripts

35

Mallet Data

Mallet data instances: Instance_id label f1 v1 f2 v2 …..

Stored in internal binary format: “vectors”

Binary format used by learners, decoders

Need to convert text files to binary format

36

Data Preparation

Built-in data importers One class per directory, one instance per file

bin/mallet import-dir --input IF --output OF Label is directory name

(Also text2vectors)

One instance per line bin/mallet import-file --input IF --output OF

Line: instance label text ….. (Also csv2vectors)

Create binary representation of text feature counts

37

Data Preparation

bin/mallet import-svmlight --input IF --output OF Allows import of user constructed feature value pairs

38

Data Preparation

bin/mallet import-svmlight --input IF --output OF Allows import of user constructed feature value pairs Format:

label f1:v1 f2:v2 …..fn:vn Features can strings or indexes

(Also bin/svmlight2vectors)

39

Data Preparation




If building test data separately from original bin/mallet import-svmlight --input IF --output OF

--use-pipe-from previously_built.vectors

40

Data Preparation




If building test data separately from original bin/mallet import-svmlight --input IF --output OF

--use-pipe-from previously_built.vectors Ensures consistent feature representation

Note: can’t mix svmlight models with others

41

Accessing Binary Formats vectors2info --input IF

42


-- print-labels TRUE Prints list of category labels in data set

43



-- print-matrix sic prints all features and values by string and number

Returns original text feature-value list Possibly out of order

44





vectors2vectors --input IF --training-file TNF --testing-file TTF --training-portion pct

45





vectors2vectors --input IF --training-file TNF --testing-file TTF --training-portion pct

Creates random training/test splits in some ratio

46

Building & Accessing Models

bin/mallet train-classifier --trainer classifiertype - -training-portion 0.9 --output-classifier OF

Builds classifier model Can also store model, produce scores, confusion matrix, etc

47


bin/mallet train-classifier --input vector_data_file --trainer classifiertype --training-portion 0.9 --output-classifier OF


--trainer: MaxEnt, DecisionTree, NaiveBayes, etc

48




--trainer: MaxEnt, DecisionTree, NaiveBayes, etc --report: train:accuracy, test:f1:en

49





Can also use pre-split training & testing files e.g. output of vectors2vectors --training-file, --testing-file

50





Confusion Matrix, row=true, column=predicted accuracy=1.0 label 0 1 |total 0 de 1 . |1 1 en . 1 |1 Summary. train accuracy mean = 1.0 stddev = 0 stderr = 0 Summary. test accuracy mean = 1.0 stddev = 0 stderr = 0

51

Accessing Classifiers

classifier2info --classifier maxent.model Prints out contents of model file

52

Accessing Classifiers

classifier2info --classifier maxent.model Prints out contents of model file

FEATURES FOR CLASS en <default> -0.036953801963395115 book 0.004605219133228236 the 0.24270652500835088 i 0.004605219133228236

53

Mallet: testing

54

Testing Use new data to test a previously built classifier bin/mallet classify-svmlight --input testfile --output

outputfile --classifier maxent.model

55


outputfile --classifier maxent.model Also instance file, directories: classify-file, classify-dir

56


outputfile --classifier maxent.model Also instance file, directories: classify-file, classify-dir Prints class,score matrix

57


outputfile --classifier maxent.model Also instance file, directories: classify-file, classify-dir Prints class,score matrix

Inst_id class1 score1 class2 score2 array:0 en 0.995 de 0.0046 array:1 en 0.970 de 0.0294 array:2 en 0.064 de 0.935 array:3 en 0.094 de 0.905

58

General Use bin/mallet import-svmlight --input svmltrain.vectors.txt

--output svmltrain.vectors Builds binary representation from feature:value pairs

59



bin/mallet train-classifier --input svmltrain.vectors –trainer MaxEnt --output-classifier svml.model

Trains MaxEnt classifier and stores model

60



bin/mallet train-classifier --input svmltrain.vectors –trainer MaxEnt --output-classifier svml.model

Trains MaxEnt classifier and stores model bin/mallet classify-svmlight --input svmltest.vectors.txt

--output - --classifier svml.model Tests on the new data

61

Other Information

Website: Download and documentation (such as it is) http://mallet.cs.umass.edu

http://mallet.cs.umass.edu/

62

Other Information

Website: Download and documentation (such as it is) http://mallet.cs.umass.edu

API tutorial: http://mallet.cs.umass.edu/mallet-tutorial.pdf

http://mallet.cs.umass.edu/

http://mallet.cs.umass.edu/mallet-tutorial.pdf

http://mallet.cs.umass.edu/mallet-tutorial.pdf

63

Text Categorization Task:

Given a document, assign to one of finite set of classes What are the classes? What are the features?

64

Text 1 Several hundred protesters, some wearing goggles and gas masks, marched

past authorities in a downtown street Sunday, hours after riot police forced Occupy Portland demonstrators out of a pair of weeks-old encampments in nearby parks.

Police moved in shortly before noon and drove protesters into the street after dozens remained in the camp in defiance city officials. Mayor Sam Adams had ordered that the camp shut down Saturday at midnight, citing unhealthy conditions and the encampment’s attraction of drug users and thieves.

Anti-Wall Street protesters and their supporters flooded a city park area in Portland early Sunday in defiance of an eviction order, and authorities elsewhere stepped up pressure against the demonstrators, arresting nearly two dozen. (Nov. 13)

More than 50 protesters were arrested in the police action, but officers did not use tear gas, rubber bullets or other so-called non-lethal weapons, police said.

Washington Post, online 11/13/2011

65

Text 2

George Washington coach Mike Lonergan looked at the stat sheet, tried to muster a smile then clicked off the reasons why the Colonials lost to No. 24 California on Sunday night.

A piercing 21-0 run by the Golden Bears at the end of the first half was at the top of the list.

Not even a second straight 20-point effort from Tony Taylor was enough to dig George Washington out of the early hole, and the Colonials spent the rest of the night in a futile game of catch-up.

“I’ve never really been involved with a run quite like that,” Lonergan said after Cal’s 81-54 win over George Washington. “I tried calling a couple timeouts. It was very disappointing that we just never really got our composure back the rest of that half. To end it that way and not even score any points, that was basically the game right there.”


66

Test 3

‘Jersey Boys’ at the National Theatre By Jane Horwitz, Sunday, November 13, 5:29 PM “Jersey Boys” is irresistible, and the touring company now at the

National Theatre gets it almost entirely right. This Broadway hit (it has been running since fall 2005 and has

played Washington before as well) rises well above the so-called jukebox show genre. Subtitled “The Story of Frankie Valli & the Four Seasons,” the musical tells a tale that transcends show business gossip to become a close character study of four talented but very different blue-collar guys from New Jersey — who just happen to have sung some of the best close-harmony rock/pop tunes of the late 1950s, the 1960s and into the 1970s.


http://www.washingtonpost.com/jane-horwitz/2011/05/22/AFQoTcAH_page.html

67

What categories?

What features?

68

Example: CoreferenceQueen Elizabeth set about transforming her husband, King

George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome his speech impediment...

69



70



Can be viewed as a classification problem

71




What are the inputs?

72





What are the categories?

73





What are the categories?

What features would be useful?

74

Example: NER Named Entity tagging:

John visited New York last Friday [person John] visited [location New York] [time last Friday]

As a classification problem John/PER-B visited/O New/LOC-B York/LOC-I last/TIME-B

Friday/TIME-I Input? Features? Classes?

Ling 570: Day 8 Classification, Mallet 1. Roadmap Open questions? Quick review of classification ...

Documents

Transcript of Ling 570: Day 8 Classification, Mallet 1. Roadmap Open questions? Quick review of classification ...