Nlp for Semantic Web

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Copyright 2009 Digital Enterprise Research Institute. All rights reserved

Digital Enterprise Research Institute www.deri.ie

Natural Language Processing

- for the Semantic Web -

Paul Buitelaar


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SemanticWebChallenge:LegacyData

LinkedData LegacyDataUnstructured,Un-Linked


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SemanticAnalysisofUnstructuredLegacyData Examplesin:SemanticSearch,Ontology-basedInformationExtraction,

OntologyLearning

NLPLayerCakewithPointers PartofSpeechTagging,Morphology,PhraseStructure SemanticTagging

WordNet,FrameNet,WordSenseDisambiguation, NamedEntities,Terms,Thesauri,Ontologies, AdvancedTopic:Ontology-LexiconInterface

GrammaticalFunctions,DependencyStructure,DiscourseAnalysis

FurtherRelevantPointers GeneralTools,Organizations,Conferences,Journals,Sites,Lists,

OverviewoftheTutorial


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MachineLearning in/forTextMining:FeatureExtractioninClustering,Classification,

TextMiningin/forInformationRetrievalSeeTutorialonInformationMining byConorHayesDERIStreamonSemanticInformationMining bringstogetherNaturalLanguageProcessingandInformationMining

WhattheTutorialwillnotaddress


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SemanticAnalysisofUnstructuredLegacyData Examplesin:SemanticSearch,Ontology-basedInformationExtraction,OntologyLearning




FurtherRelevantPointers Conferences,Journals,Websites,MailingLists,



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SemanticAnnotation&Search

MuchMore(2000-2003) Semanticannotationofasetofmedicalscientificabstracts&patientrecordsasqueriesacross

languages(English,German)

OntologyLearning OntoLT(2004-2005) Extractionofclasses,subclassesandrelations(objectproperties)fromalinguisticallyannotated

documentset

Ontology-basedInformationExtraction SmartWeb(2005-2008) Extractionofentitiesandeventsfromasetoffootballmatchreports

SemanticVideoBrowsing K-Space(2007-2008) Extractionofentitiesandeventsfromasetoffootballmatchreports,alignedwithfootballvideo,

enablingsemantic-levelvideoindexingandbrowsing

OpenCalais,GIST Industrialstrengthopensource/commercialsemanticannotation&retrieval

SomeExampleApplications


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SemanticAnnotation&SearchMuchMore


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13inguisticStructure2OntologyMappingRules

OntologyLearningOntoLT


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14 Extraction&InspectContexts



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15 ExtractOntologyFragments



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OntologyLearningOntoLTGerman Clinical Report:

An 40 Kniegelenkprparaten wurden mittlere Patellarsehnendrittel mit einerneuen Knochenverblockungstechnik in einem

zweistufigen Bohrkanal bzw. mitkonventioneller Interferenzschraubentechnikfemoral fixiert.

English Translation:

In 40 human cadaver knees, mid patellarligament thirds were fixedwith a trapezoidbone block on one side on the femoral side

in a two-level drill hole, or with aconventional interference screw.

LinguisticAnnotation(fragments)


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Ontology-basedInformationExtractionSmartWeb

semistruct#Deutschland_vs_Brasilien_30_Juni_2002_18:00[

sportevent#matchEvents -> soba#ID11 ].

soba#ID11:sportevent#Parry[

sportevent#committedBy ->

semistruct#Deutschland_vs_Brasilien_30_Juni_2002_18:00_Oliver_Kahn_PFP ].

Information Extraction

Ontology Population

Oliver Kahn konnte den Schuss von Beto halten.

Oliver Kahn could stop the shot by Beto.


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Ontology-basedInformationExtractionSmartWeb


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A/V Feature

Analysis

ExtractedEntities and Events

Minute-by-Minute Match Reports

Non-Linear Event and Entity Browsing

SemanticVideoBrowsingK-Spacehttp://keg.vse.cz/wf/kspace/smil/


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IndustrialApplicationsGIST(CALAIS)


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Open Calais Extracts Entities, Facts


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With a split decision in thefinal two primaries and a

flurry of superdelegateendorsements, Sen. BarackObama sealed theDemocratic presidentialnomination Tuesday nightafter a grueling, history-making campaign that willmake him the first AfricanAmerican to head a major-

party ticket.

Before a chanting, cheering

audience in St. Paul, Minn.,the first-term Illinois senatorsavored what once seemedan unlikely outcome to theDemocratic race againstSen. Hillary RodhamClinton. He now facesanother hard-fought battle,against Sen. John McCain,the presumptive Republicancandidate.

Open Calais Extracts Entities, Facts


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OntologyLearning

NLPLayerCakewithPointers PartofSpeechTagging,Morphology,PhraseStructure

SemanticTagging WordNet,FrameNet,WordSenseDisambiguation, NamedEntities,Terms,Thesauri,Ontologies, AdvancedTopic:Ontology-LexiconInterface


FurtherRelevantPointers Conferences,Journals,Websites,MailingLists,



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NLP-ACompleteExampleHe booked the large table in the corner.S

heNPSubject , AgentX

booked the large table in the cornerVP

... It was still available.S

hePronoun3rd PersonAnimate

the large table in the cornerNP

Direct Object, PatientDefinite Y

the large tableNP

in the cornerPP

tableNounSingularHeadfurniture_01

the cornerNPDefinite Z

inPrepositionHeadPredicate

largeAdjectiveModifier

was still availableVP

itNPSubject, PatientY

itPronoun3rd PersonInanimate

bookVerbPast, 3rd PersonHeadPredicate

isVerbPast, 3rd PersonHeadPredicate

still availableAdvP


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NLPLayerCake

Tezao

[table]Hebookedthelargetableinthecorner...

PoS

hTn

[table:noun]

MopocaAys

[table~s]work~ing][Sommer~schule]

SmacTn

[table:ARTIFACT,furniture_01]

Pae

[[[the][large][table]NP][[in][the][corner]PP]NP]

D Sruue

[[the:SPEC][large:MOD][table:HEAD]NP][[He:SUBJ][booked:PRED][[this][table:HEAD]NP:DOBJ]S]

Dsose

Ays

[He:SUBJ][booked:PRED]this[[table:HEAD]NP:DOBJ:X1][[It:SUBJ:X1][was:PRED]available]


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Tokenization Where are the words?

Part of Speech Tagging Is this word a verb or a noun or something else?

Morphology Can I split this word up?

Phrase Structure Do these words go together?

Semantic Tagging What objects are expressed by the words/phrases in the sentence?

Grammatical Functions & Dependency Structure Which objects do what? And in relation to which others?

Discourse Analysis Which events are expressed throughout a text/discourse? How do they interact? And which objects are

involved?

NLPLayers


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Annotate each word in a sentence with a part-of-speech (PoS) tag -useful for subsequent syntactic parsing

Most common PoS tag set for English is Penn Treebank set of 45 tags,e.g.

John saw the saw and decided to take it to the table.NNP VBD DT NN CC VBD TO VB PRP IN DT NN

Other tag sets in use for other languages, e.g. Stuttgart-Tbingen TagSet (STTS) for German Challenge in Part-of-Speech Tagging is ambiguity

like can be Verb: I like/VBP candy. Preposition: Time flies like/IN an arrow.

around can be Preposition: I bought it at the shop around/IN the corner. Particle: I never got around/RP to getting a car. Adverb: A new Prius costs around/RB $25K.

Part-of-SpeechTaggingAdaptedfrom:http://www.cs.utexas.edu/~mooney/cs388/slides/pos-tagging.ppt


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Noun (person, place or thing) Singular (NN): dog, fork Plural (NNS): dogs, forks Proper (NNP, NNPS): John, Springfields Personal pronoun (PRP): I, you, he, she, it Wh-pronoun (WP): who, what

Verb (actions and processes) Base, infinitive (VB): eat Past tense (VBD): ate Gerund (VBG): eating Past participle (VBN): eaten Non 3rd person singular present tense (VBP): eat 3rd person singular present tense: (VBZ): eats Modal (MD): should, can To (TO): to (to eat)

Adjective (modify nouns) Basic (JJ): red, tall Comparative (JJR): redder, taller Superlative (JJS): reddest, tallest

Adverb (modify verbs) Basic (RB): quickly Comparative (RBR): quicker Superlative (RBS): quickest

Preposition (IN): on, in, by, to, with

Determiner: Basic (DT) a, an, the WH-determiner (WDT): which, that

Coordinating Conjunction (CC) and, but, or

Particle (RP) off (took off), up (put up)

Part-of-SpeechTaggingPoSEnglishAdaptedfrom:http://www.cs.utexas.edu/~mooney/cs388/slides/pos-tagging.ppt


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Closed classcategories are composed of a small, fixed set ofgrammatical function words for a given language: Pronouns (it, he, she, ) Prepositions (on, for, from, to, ) Modals (will, can, may, ) Determiners (a, the) Particles (to, up, off) Conjunctions (and, or)

Open classcategories are composed of large sets of content words andare open to new additions:

Nouns (a googler) Verbs (to google) Adjectives (geeky)

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Part-of-SpeechTaggingClosedvs.OpenAdaptedfrom:http://www.cs.utexas.edu/~mooney/cs388/slides/pos-tagging.ppt


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Part-of-SpeechTaggingStateOfTheArt Overview of available PoS taggers:

http://www-nlp.stanford.edu/links/statnlp.html#Taggers

Many are widely-used (often retrainable), a very small selection: TreeTagger, decision trees, free research license

http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger

TnT, Thorsten Brants, HMM, free research licensehttp://www.coli.uni-saarland.de/~thorsten/tnt/

ENGCG, lexicon & rules, commercial (LingSoft)http://www2.lingsoft.fi/cgi-bin/engcg


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Some definitions Morphological Analysis split up words into component morphemes

and build a (formal) representation of word-internal structure

Morpheme minimal meaning-bearing unit in a language Stem morpheme that forms central meaning unit in a word Affix word element that can only occur attached to a stem

Prefix specificunspecific(English)

Suffix wonderwonderful(English)

Infix hingihumingi(Tagalog)

Circumfix sagen gesagt(German)

Morphological complexity varies between languages

Isolated languages (no morphology): e.g., Chinese Morphologically poor languages: e.g., English Morphologically complex languages: e.g., Turkish

MorphologicalAnalysisAdaptedfrom:http://courses.washington.edu/ling570/fei_fall07/10_15_morph.ppt


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Inflection: stem + morpheme (same PoS class) writing write + V + Progressive books book + N + Plural writes write + V + 3rd Person + Singular flies fly + N + Plural

fly + V + 3rd Person + Singular

Derivation: stem + morpheme (different PoS class) civil civilized civilization

Compounding: multiple stems cabdriver cab + driver doghouse dog + house Flachbildschirm (flat screen) flach + Bildschirm (flat screen)

Flachbild + Schirm (flat view screen)

flach + Bild + Schirm (flat picture screen) Cliticization: stem + clitic

Ive I + have

MorphologicalAnalysisOverview,AmbiguityAdaptedfrom:http://courses.washington.edu/ling570/fei_fall07/10_15_morph.ppt


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Phrase Group of words that functions as a single unit in syntax (Wikipedia)

NP : Noun Phrase (the car, a clever student) VP : Verb Phrase (study hard, play the guitar) PP : Prepositional Phrase (in the class, above the earth) AP : Adjective Phrase (very tall, incredibly large)

Phrase Structure Analysis

Breaking up a sentence into recursively defined coherent units (constitutionalparts), e.g., an NP consisting of several NPs

First step in sentence parsing (see also further NLP layers) Chunks

Non-recursive phrases, as introduced by shallow parsing approach

Chunking Also known as shallow parsing (without overall sentence structure &

grammatical functions see also further NLP layers)

PhraseStructureAnalysisDefinitionsWithinputfrom:http://www.kwary.net/linguistics/gl/GLSyntax01.ppt


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NP (Det) N

NP

the

NDet

bus

PP

in

NPP

the

Det N

yard

PhraseStructureAnalysisNP,PPExampleAdaptedfrom:http://www.kwary.net/linguistics/gl/GLSyntax01.ppt

PP P NP

NP

the

NDet

bus

PP

in

NPP

the

Det N

yard

NP (Det) N (PP)


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PhraseStructureAnalysisVPExampleAdaptedfrom:http://www.kwary.net/linguistics/gl/GLSyntax01.ppt

VP V (NP) (PP)VP

took

NPV PP

from

NPP

the

Det N

bankthe

Det N

money


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PhraseStructureAnalysisStateOfTheArt Overview on Parsers (including shallow parsing) for English

http://www.aclweb.org/aclwiki/index.php?title=Parsers_for_English

Overview for other languages http://www.aclweb.org/aclwiki/index.php?title=List_of_resources_by_language

Some shallow parser demos on the web: TreeTagger (PoS, Chunking), decision trees, free research license

http://www.ims.uni-stuttgart.de/projekte/corplex/TreeTagger

CNTS Memory Based Shallow Parser (Univ. of Antwerpen), classifier, license?http://www.cnts.ua.ac.be/cgi-bin/jmeyhi/MBSP-instant-webdemo.cgi

Univ. of Illinois at Urbana-Champaign, classifier, license?http://l2r.cs.uiuc.edu/~cogcomp/shallow_parse_demo.php


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Definition and History Classification of words, phrases with a semantically defined

category

Nowadays associated with Semantic Web (semanticannotation, knowledge markup) and Web 2.0 tagging

In NLP refers to assigning a sense to a word or phrase

Sense sets defined by Originally, machine readable dictionaries, e.g., LDOCE Recent years, wordnets (nouns), framenets (verbs) Increasingly, general & domain ontologies

SemanticTagging


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WordNet is a Semantic Lexicon & Lexical Database Organized around meaning rather than word forms Maps words to meanings/interpretations or senses Senses are represented by synsets (sets of synonyms), e.g.,

{board, plank} : piece of lumber {board, committee} : group of people

Machine readable (has a formal structure) Freely downloadable: http://wordnet.princeton.edu/

Integrated wordnets for several European languages EuroWordNet: http://www.illc.uva.nl/EuroWordNet/

Wordnets for many languages with interoperable format http://www.globalwordnet.org/gwa/wordnet_table.htm

SemanticTaggingWordNet


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In 1985 a group of psychologists and linguists at Princeton University undertookto develop a lexical database

The initial idea was to provide an aid to use in searching dictionariesconceptually, rather than merely alphabetically

WordNet instantiates hypotheses based on results of psycholinguistic research In anomic aphasia, there is a specific inability to name objects.

When confronted with an apple, say, patients may be unable to utter apple, eventhough they will reject such suggestions as shoe or banana, and will recognize thatapple is correct when it is provided.

Caramazza/Berndt 1978

expose such hypotheses to the full range of the common vocabulary

Miller, George A., Richard Beckwith, Christiane Fellbaum, Derek Gross and Katherine J. Miller. Introduction

to WordNet: an on-line lexical database. In: International Journal of Lexicography 3 (4), 1990, pp. 235 - 244.

SemanticTaggingWordNetOrigin


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Synsets represent different Senses Words that occur in several synsets have a corresponding

number of senses i.e. are ambiguous:

SemanticTaggingSynsets,Senses


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Homonymy Unrelated Senses, e.g.

The ball went over the fence - artifact The ball went on into the late hours - event

Systematic Polysemy Related Senses, e.g.

The Boston office has been newly decorated - building The Boston office was founded in 1985. - organization The Boston office called. - group-of-people

Also referred to in the literature as regular polysemy (Apresjan 1973) or

logical polysemy (Pustejovsky 1991, 1995 ) systematic polysemyintroduced by (Nunberg & Zaenen 1992) - see also Bierwisch 1983 (schoolexample), Hobbs et al 1993 (office example)

SemanticTaggingAmbiguitycont.


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Synsets are organized in hierarchies, defining: generalization (hypernymy) specialization (hyponymy)

Example{entity}

{whole, unit}

{building material}

{lumber, timber}{board, plank}

hyponymy

hypernymy

SemanticTaggingSynsetHierarchy


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SemanticTaggingHierarchyExample


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frameplacing:AgentplacesaThemeatalocation,theGoalDavidarrangedhisbriefcaseonthefloor.

archive.v,arrange.v,bag.v,bestow.v,billet.v,bin.v,bottle.v,box.v,brush.v,cage.v,cram.v,crate.v,dab.v,

daub.v,deposit.v,drape.v,drizzle.v,dust.v,embed.v,emplace.v,file.v,garage.v,hang.v,heap.v,

immerse.v,implant.v,inject.v,insert.v,insertion.n,jam.v,lay.v,lean.v,load.v,lodge.v,mount.v,pack.v,

package.v,park.v,perch.v,pile.v,place.v,placement.n,plant.v,plunge.v,pocket.v,position.v,pot.v,put.v,rest.v,rub.v,set.v,sheathe.v,shelve.v,shoulder.v,shower.v,sit.v,situate.v,smear.v,sow.v,stable.v,

stand.v,stash.v,station.v,stick.v,stow.v,stuff.v,tuck.v,warehouse.v,wrap.v

framearranging:AgentputsacomplexThemeintoaparticularConfigurationDavidarrangedthestonesinacircle.

arrange.v,arrangement.n,array.v,deploy.v,deployment.n,format.v,setup.v

SemanticTaggingFrameAmbiguity


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Word Sense Disambiguation Classification of the correct sense to a word Based on wordnets & similar resources for many languages Sense-annotated corpora enable classifier training No longer very active area of research in NLP community Annotated corpora, tools, evaluation data sets available

from SenseVal (1-4) evaluation campaigns:

http://www.senseval.org/ Recently attention turned to Semantic Role Labelling and

variety of other tasks in Computational Lexical Semantics

see SemEval evaluation campaign: http://semeval2.fbk.eu/

SemanticTaggingWordSense


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Semantic Role Labelling Classification of correct frame category (sense) to a verb &

assign semantic roles to its syntactic arguments

Based on FrameNet availability and similar resources, e.g. PropBank http://verbs.colorado.edu/~mpalmer/projects/ace.html NomBank http://nlp.cs.nyu.edu/meyers/NomBank.html VerbNet http://verbs.colorado.edu/~mpalmer/projects/verbnet.html SemLinkhttp://verbs.colorado.edu/semlink/ OntoNoteshttp://www.bbn.com/ontonotes/ German FrameNethttp://www.coli.uni-saarland.de/projects/salsa

Frame-annotated corpora enable classifier training Recently very active area of research in NLP community

SemanticTaggingSemanticRoles


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SemanticTaggingStateOfTheArt Word Sense Disambiguation tools, selection

WSD tools by Ted Pedersen (University of Minnesota, Duluth), freehttp://sourceforge.net/projects/wsdgate/ & others

SenseLearner, Rada Mihalcea (Univ. of North Texas), freehttp://www.cse.unt.edu/~rada/downloads.html#senselearner

SuperSenseTagger, SemTechLab Rome ?, license?http://sourceforge.net/projects/supersensetag/

Semantic Role Labelling tools, selection Shalmaneser (Saarland Univ.), pluggable parsing & classifiers, free license

http://www.coli.uni-saarland.de/projects/salsa/shal/

Univ. of Illinois at Urbana-Champaign, parsing & classifiers, license?http://l2r.cs.uiuc.edu/~cogcomp/srl-demo.php

SWIRL (Universitat Politecnica de Catalunya), parsing & classifiers, GPL licensehttp://www.surdeanu.name/mihai/swirl


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Metonymy (part stands for whole) The Boston office called. to call expects an object of type Human in Agent position coerce office into an object of type (Group-of) Person > Human lexical semantic inference: Person Work-at Office

SemanticTaggingLexicalInference

office

Office

Organization

Building

Person

Has-addressLocated-at

Representation-of

Work-at

Work-for


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Metonymy in Bridging (of discourse referents) Peter bought a car. The engine runs well. the engine refers to already introduced object (discourse referent) lexical semantic inference: Engine Part-of Car

SemanticTaggingLexicalInference

Car

EnginePart-of

Has-partcar


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University

Schoolis_part_of

Campuslocated_at

label label

school staff

Studentstudies_at

Staff

works_at

Semantic Tagging Ontologies


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University

Schoolis_part_of

Campuslocated_at

has_German_termhas_US-English_term has_Dutch_term

FakulttSchool Faculteit

Studentstudies_at

Staff

works_at

Semantic Tagging Classes, Terms

RDF(S) & OWL current status


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LingInfoOralMucosa

hasLingInfo

Term-1

Mundschleimhaut

hasOrthographicForm

DE

hasLang

hasMorphSynInfo

WordForm-1

N

hasPoS

Term-2 Term-3

hasStem

Mund Schleimhaut

hasOrthographicForm

WordForm

instanceOf

hasMorphSynInfo

Mucosa

hasLingInfo

instanceOf

Semantic Tagging Lexicalized Ontologies

http://olp.dfki.de/LingInfo/

http://ontoware.org/projects/lexonto/


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Semantic tagging beyond word senses & semantic roles Terms, Classes, Relations, Properties/Attributes Names

Terms, Classes, Relations, Properties/Attributes Semantic annotation on the basis of a thesaurus or ontology Term recognition & extraction

terms are domain-specific phrases Relation extraction

relations are domain-specific semantic roles Ontology-based information extraction

SemanticTaggingTerms,Classes


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SemanticTagging Terms,Relations

GENIA Relation GENIA Term(Class)

SemanticRole

GrammaticalFunction

inhibit interleukin 1 beta

IL-1beta

Agent Subject

insulin secretion Target Direct Object

Withinputfrom:http://www.lrec-conf.org/proceedings/lrec2008/slides/496.ppt

Terms/Classes & Relations in genetics domain GENIA corpus

http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/home/wiki.cgi?page=GENIA+corpus

Examples Interleukin 1 beta inhibits insulin secretion IL-1beta is known to inhibit insulin secretion Insulin secretion is inhibited by IL-1 beta

Term recognition & extraction Grammatical function annotation: subject, direct object, etc.

see further NLP layers


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Eurovoc ThesaurusTerminology in all EU languages on all

EU areas: politics, trade, law, science,energy, agriculture,

MT 3606 natural and applied sciencesUF gene pool

genetic resourcegenotypeheredity

BT1 biologyBT2 life sciencesNT1 DNART genetic engineering (6411)

SemanticTaggingTerms,ClassesMedical Subject Headings (MeSH)Thesaurus with taxonomy of ~ 250,000 terms,

representing medical subjects for retrievalpurposes

MeSH Heading Databases, GeneticEntry Term Genetic DatabasesEntry Term Genetic Sequence DatabasesEntry Term OMIMEntry Term Mendelian Inheritance in Man

Entry Term Genetic Data BanksEntry Term Genetic Data BasesEntry Term Genetic Information DatabasesSee Also Genetic Screening

Gene Ontology

Accession GO:0009292Synonyms broad : genetic exchange

Term Lineage all : all (164142)GO:0008150 : biological process (115947)

GO:0007275 : development (11892)GO:0009292 : genetic transfer (69)


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Semantic tagging beyond word senses & semantic roles Terms, Classes, Relations, Properties/Attributes Names

Semantic annotation of names Named Entity Recognition Originally intended as extension of Tokenization, e.g. in

recognizing Names and other specific tokens such as Dates, Times

Evolved into a more general identification and classification ofnames of People, Organisations, Companies, Countries, Cities, etc.

Currently merging with ontology-based information extraction

SemanticTaggingNames


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SemanticTaggingStateOfTheArtcont. Named Entity Recognition

Good overview of many available toolshttp://en.wikipedia.org/wiki/Named_entity_recognition

Semantic annotation with thesauri, ontologies in various domains, e.g., Annotate biomedical text with UMLS Metathesaurus

MetaMap (US National Library of Medicine), free license

http://mmtx.nlm.nih.gov/

Annotate business text with KIM ontologyKIM (Ontotext), free research license

http://www.ontotext.com/kim/

Annotate football (soccer) text with SWIntO ontologySProUT (DFKI), free research license

http://www.dfki.de/sw-lt/heartofgold/ (web demo)


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Parsing parsing, or syntactic analysis, is the process of analyzing a

sequence of tokens to determine their grammatical structure withrespect to a given grammar (Wikipedia)

Shallow parsing (discussed above) provides Part of Speech tags Non-recursive phrases (chunks)

Full (or deep) parsing provides on top of this Constituent structure

complete syntactic structure in terms of interconnected recursive phrases and/or Clause structure

predicate (mostly a verb) and one or more syntactic arguments (phrases) grammatical functions for predicate arguments: subject, direct object,

and/or Dependency structure head-modifier analysis, semantic roles

ParsingOverview


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ParsingFullParseExampleHe booked the large table in the corner.S

heNPSubject, Agent


hePronoun

3rd

personAnimate


Direct Object, Patient

the large tableNP

in the cornerPP


the cornerNPinPrepositionHeadPredicate


bookVerb

Past, 3rd

personHeadPredicate


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heNPSubject, Agent


hePronoun

3rd

personAnimate



the large tableNP

in the cornerPP




bookVerb

Past, 3rd

personHeadPredicate

Part of Speech

Morphology


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heNPSubject, Agent


hePronoun

3rd

personAnimate



the large tableNP

in the cornerPP




bookVerb

Past, 3

rd

personHeadPredicate

Phrases


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heNPSubject, Agent


hePronoun

3

rd

personAnimate



the large tableNP

in the cornerPP




bookVerb

Past, 3

rd

personHeadPredicate

Predicates

GrammaticalFunctions


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heNPSubject, Agent


hePronoun

3

rd

personAnimate



the large tableNP

in the cornerPPModifier




bookVerb

Past, 3

rd

personHeadPredicate

Semantic Tags

Semantic Roles


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heNPSubject, Agent


hePronoun

3

rd

personAnimate



the large tableNP

in the cornerPPModifier




bookVerbPast, 3rd personHeadPredicate

Head-ModifierAnalysis


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ParsingDependencyStructure

hePronoun3rd personAnimate


largeAdjectiveModifierSize

bookVerbPast, 3rd personHeadPredicate

cornerNounSingularModifierLocation

Agent Patient

Size Location

He booked the large table in the corner.


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ParsingDependencyStructureforIE

hePronoun3rd person

Animate

tableNounSingular

Head

furniture_01


Size

bookVerbPast, 3rd person

Head

Predicate

cornerNounSingular

Modifier

Location

Agent Patient

Size Location

Class + Properties Extracted Objects & Values Source

Booking x, y PredicateBooking-sponsor Male(x) AgentBooking-order Table(y), Size(large), Location(y,z), Corner(z) Patient

He booked the large table in the corner.


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ParsingStateOfTheArt Widely-used parsers

MINIPAR, Dekang Lin, free research license Download: http://www.cs.ualberta.ca/~lindek/minipar.htm Web demo: http://dbis.nankai.edu.cn/miniparweb/

Stanford Parser, Klein/Manning, free research license http://nlp.stanford.edu/software/lex-parser.shtml Web demo: http://nlp.stanford.edu:8080/parser/

Rasp Parser (Sussex Univ.), Briscoe/Carroll, free research license http://www.informatics.susx.ac.uk/research/groups/nlp/rasp/

Link Grammar Parser (CMU), Temperley et al., free license http://www.link.cs.cmu.edu/link/ Web demo: http://nlp.stanford.edu:8080/parser/


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Linking event participants (Semantic Role fillers) within andacross sentences, i.e.,

an anaphor can be linked back to a discourse referent thatserves as its antecedent, e.g.,

He bought a bottle of wine, sat down on a stone, and drank it.

he AND it are anaphora

a bottle of wine AND a stone introduce discourse referents

it can be linked back to antecedent a bottle of wine OR a stone

DiscourseAnalysisAnaphoraResolutionWithinputfrom:http://www.ling.su.se/DaLi/education/courses/ngslt_nlp06/PragmaticsGSLTLecture06.ppt


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He booked the large table in the corner.S

heNPSubject , AgentX


... It was still available.S

hePronoun3rd PersonAnimate

the large table in the cornerNPDirect Object, PatientDefinite Y

the large tableNP

in the cornerPP


the cornerNPDefinite Z

inPrepositionHeadPredicate


was still availableVP

itNPSubject, PatientY

it

Pronoun3rd PersonInanimate

bookVerbPast, 3rd PersonHeadPredicate

is

VPast, 3rd PersonHeadPredicate

still availableAdvP

DiscourseAnalysisAnaphoraResolution


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Linking events in terms of temporal sequence, causality etc., e.g.,

John bought a Mercedes, so Bill leased a BMW. (temporal sequence)

John hid Bills car keys as he had drunk too much. (causality)

DiscourseAnalysisDiscourseStructureWithinputfrom:http://www.ling.su.se/DaLi/education/courses/ngslt_nlp06/PragmaticsGSLTLecture06.ppt


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DiscourseAnalysisStateOfTheArt No readily available black-box tools Anaphora resolution often built-in functionality in NER,

parsing, etc.

To experiment with discourse referents, anaphoraresolution etc., try out e.g. Boxer

Johan Bos, Univ. of Rome http://svn.ask.it.usyd.edu.au/trac/candc/wiki/boxer


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OntologyLearning




FurtherRelevantPointers GeneralTools,Organizations,Conferences,Journals,Sites,Lists,



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GATE, Univ. of Sheffield Eclipse of Natural Language Engineering http://gate.ac.uk/

UIMA, IBM / OpenSource 'Open, Industrial-Strength Platform for Unstructured Information Analysis and Search http://incubator.apache.org/uima/

NLTK (Natural Language Toolkit), Melbourne Univ. ? Open source Python modules for research and development in natural language

processing - book (June 2009): Natural Language Processing with Python http://www.nltk.org/

MBT: Memory-based tagger-generator and tagger, Univ. of Tilburg/Antwerpen can generate a sequence tagger on the basis of a training set of tagged sequences http://ilk.uvt.nl/mbt/

SProUT, DFKI platform for development of multilingual shallow text processing and information

extraction systems

http://sprout.dfki.de/

FurtherRelevantPointersGeneralTools


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Conferences Association for Computational Linguistics

ACL (Int.), EACL (Europe), NAACL (North-America), IJCNLP (AFNLP - Asia) http://www.aclweb.org/ ACL SIGS: http://aclweb.org/aclwiki/index.php?title=Special_interest_groups

International Conference on Computational Linguistics COLING: http://nlp.shef.ac.uk/iccl/

International Conference on Language Resources and Evaluation

LREC: http://www.lrec-conf.org/

Other NLP conferences: EMNLP, CONLL, RANLP, CICLing,

Journals Computational Linguistics, MIT Press Natural Language Engineering, Cambridge University Press

Journal of Logic, Language and Information, Springer Language Resources and Evaluation, Springer

FurtherRelevantPointersPublications


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Handbooks Handbook of natural language processing, CRC Press, 2000 new editionin progress (2009) Speech and Language Processing: An Introduction to Natural Language

Processing, Computational Linguistics, and Speech Recognition, PrenticeHall, 2008

The Oxford handbook of computational linguistics, Oxford University Press,2005

Foundations of statistical natural language processing, MIT Press, 2003 Relevant Mailing Lists

Corpora list: http://gandalf.aksis.uib.no/corpora/ Linguist list: http://linguistlist.org/

Other NLP sites - broad overviews of tools, resources, people ACL Wiki: http://aclweb.org/aclwiki LT World: http://www.lt-world.org/

FurtherRelevantPointersMoreReading


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Thanks!

Further Questions:

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