ttic 31190: natural language...

TTIC31190:NaturalLanguageProcessing

KevinGimpelWinter2016

Lecture1:Introduction

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Whatisnaturallanguageprocessing?

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anexperimentalcomputerscienceresearchareathatincludesproblemsandsolutionspertainingto

theunderstandingofhumanlanguage

Whatisnaturallanguageprocessing?

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TextClassification

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TextClassification

• spam/notspam• prioritylevel• category(primary/social/promotions/updates)

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SentimentAnalysis

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MachineTranslation

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MachineTranslation

NewPoll:WillyoubuyanAppleWatch?

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QuestionAnswering

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Summarization

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Summarization

TheAppleWatchhasdrawbacks.Thereareothersmartwatches thatoffermorecapabilities.

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DialogSystems

user:ScheduleameetingwithMattandDavidonThursday.computer:Thursdaywon’tworkforDavid.HowaboutFriday?user:I’dpreferMondaythen,butFridaywouldbeokifnecessary.

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Part-of-SpeechTagging

determinerverb(past)prep.properproperposs.adj.nounSomequestionedifTimCook’sfirstproduct

modalverbdet.adjectivenounprep.properpunc.wouldbeabreakawayhitforApple.

determinerverb(past)prep.properproperposs.adj.noun

modalverbdet.adjectivenounprep.properpunc.

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determinerverb(past)prep.nounnounposs.adj.nounSomequestionedifTimCook’sfirstproduct

modalverbdet.adjectivenounprep.nounpunc.wouldbeabreakawayhitforApple.

determinerverb(past)prep.properproperposs.adj.noun

modalverbdet.adjectivenounprep.properpunc.

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determinerverb(past)prep.nounnounposs.adj.nounSomequestionedifTimCook’sfirstproduct

modalverbdet.adjectivenounprep.nounpunc.wouldbeabreakawayhitforApple.

SomequestionedifTimCook’sfirstproductwouldbeabreakawayhitforApple.

NamedEntityRecognition

PERSON ORGANIZATION

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SyntacticParsing

figurecredit:Durrett &Klein(2014)17

Revenues of $14.5 billion were posted by Dell1. The company1 ...

en.wikipedia.org/wiki/Dell

en.wikipedia.org/wiki/Michael_Dell

Infobox type: company

Infobox type: person

ORGANIZATIONPERSON

Figure 1: Coreference can help resolve ambiguous casesof semantic types or entity links: propagating informationacross coreference arcs can inform us that, in this context,Dell is an organization and should therefore link to thearticle on Dell in Wikipedia.

shown that tighter integration of coreference andentity linking is promising (Hajishirzi et al., 2013;Zheng et al., 2013); we extend these approaches andmodel the entire process more holistically. Namedentity recognition is improved by simple coreference(Finkel et al., 2005; Ratinov and Roth, 2009) andknowledge from Wikipedia (Kazama and Torisawa,2007; Ratinov and Roth, 2009; Nothman et al.,2013; Sil and Yates, 2013). Joint models of corefer-ence and NER have been proposed in Haghighi andKlein (2010) and Durrett et al. (2013), but in neithercase was supervised data used for both tasks. Tech-nically, our model is most closely related to that ofSingh et al. (2013), who handle coreference, namedentity recognition, and relation extraction.2 Our sys-tem is novel in three ways: the choice of tasks tomodel jointly, the fact that we maintain uncertaintyabout all decisions throughout inference (rather thanusing a greedy approach), and the feature sets wedeploy for cross-task interactions.

In designing a joint model, we would like topreserve the modularity, efficiency, and structuralsimplicity of pipelined approaches. Our model’sfeature-based structure permits improvement of fea-tures specific to a particular task or to a pair of tasks.By pruning variable domains with a coarse modeland using approximate inference via belief propaga-tion, we maintain efficiency and our model is only afactor of two slower than the union of the individual

2Our model could potentially be extended to handle relationextraction or mention detection, which has also been addressedin past joint modeling efforts (Daume and Marcu, 2005; Li andJi, 2014), but that is outside the scope of the current work.

models. Finally, as a structured CRF, it is concep-tually no more complex than its component modelsand its behavior can be understood using the sameintuition.

We apply our model to two datasets, ACE 2005and OntoNotes, with different mention standardsand layers of annotation. In both settings, our jointmodel outperforms our independent baseline mod-els. On ACE, we achieve state-of-the-art entity link-ing results, matching the performance of the systemof Fahrni and Strube (2014). On OntoNotes, wematch the performance of the best published coref-erence system (Bjorkelund and Kuhn, 2014) andoutperform two strong NER systems (Ratinov andRoth, 2009; Passos et al., 2014).

2 Motivating Examples

We first present two examples to motivate our ap-proach. Figure 1 shows an example of a case wherecoreference is beneficial for named entity recogni-tion and entity linking. The company is clearlycoreferent to Dell by virtue of the lack of other possi-ble antecedents; this in turn indicates that Dell refersto the corporation rather than to Michael Dell. Thiseffect can be captured for entity linking by a fea-ture tying the lexical item company to the fact thatCOMPANY is in the Wikipedia infobox for Dell,3

thereby helping the linker make the correct decision.This would also be important for recovering the factthat the mention the company links to Dell; how-ever, in the version of the task we consider, a men-tion like the company actually links to the Wikipediaarticle for Company.4

Figure 2 shows a different example, one wherethe coreference is now ambiguous but entity linkingis transparent. In this case, an NER system basedon surface statistics alone would likely predict thatFreddie Mac is a PERSON. However, the Wikipediaarticle for Freddie Mac is unambiguous, which al-lows us to fix this error. The pronoun his can then becorrectly resolved.

These examples justify why these tasks should behandled jointly: there is no obvious pipeline orderfor a system designer who cares about the perfor-

3Monospaced fonts indicate titles of Wikipedia articles.4This decision was largely driven by a need to match the

ACE linking annotations provided by Bentivogli et al. (2010).

Coreference Resolution

EntityLinking

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“Winograd Schema”Coreference Resolution

Themancouldn'tlifthissonbecausehewassoweak.

Themancouldn'tlifthissonbecausehewassoheavy.

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“Winograd Schema”Coreference Resolution

Themancouldn'tlifthissonbecausehewassoweak.

Themancouldn'tlifthissonbecausehewassoheavy.

man

son

OncetherewasaboynamedFritzwholovedtodraw.Hedreweverything.Inthemorning,hedrewapictureofhiscerealwithmilk.Hispapasaid,“Don’tdrawyourcereal.Eatit!”Afterschool,Fritzdrewapictureofhisbicycle.Hisunclesaid,“Don'tdrawyourbicycle.Rideit!”…

WhatdidFritzdrawfirst?A)thetoothpasteB)hismamaC)cerealandmilkD)hisbicycle

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ReadingComprehension

Conspicuousbytheirabsence…• speechrecognition(seeTTIC31110)• informationretrievalandwebsearch• knowledgerepresentation• recommendersystems

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ComputationalLinguisticsvs.NaturalLanguageProcessing

• howdotheydiffer?

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ComputationalBiologyvs.Bioinformatics

“Computationalbiology=thestudyofbiologyusingcomputationaltechniques.Thegoalistolearnnewbiology,knowledgeaboutlivingsystems.Itisaboutscience.

Bioinformatics=thecreationoftools(algorithms,databases)thatsolveproblems.Thegoalistobuildusefultoolsthatworkonbiologicaldata.Itisaboutengineering.”

--RussAltman

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ComputationalLinguisticsvs.NaturalLanguageProcessing

• manypeoplethinkofthetwotermsassynonyms

• computationallinguisticsismoreinclusive;morelikelytoincludesociolinguistics,cognitivelinguistics,andcomputationalsocialscience

• NLPismorelikelytousemachinelearningandinvolveengineering/system-building

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IsNLPScienceorEngineering?• goalofNLPistodeveloptechnology,whichtakestheformofengineering

• thoughwetrytosolvetoday’sproblems,weseekprinciplesthatwillbeusefulforthefuture

• ifscience,it’snotlinguisticsorcognitivescience;it’sthescienceofcomputationalprocessingoflanguage

• soIliketothinkthatwe’redoingthescienceofengineering

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CourseOverview• Newcourse,firsttimebeingoffered

• Aimedatfirst-yearPhDstudents

• Instructorofficehours:Mondays3-4pm,TTIC531

• Teachingassistant:Lifu Tu,TTICPhDstudent

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Prerequisites• Nocourseprerequisites,butIwillassume:– someprogrammingexperience(nospecificlanguagerequired)

– familiaritywithbasicsofprobability,calculus,andlinearalgebra

• Undergraduateswithrelevantbackgroundarewelcometotakethecourse.Pleasebringanenrollmentapprovalformtomeifyoucan’tenrollonline.

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Grading• 3assignments(15%each)• midtermexam(15%)• courseproject(35%):– preliminaryreportandmeetingwithinstructor(10%)– classpresentation(5%)– finalreport(20%)

• classparticipation(5%)• nofinal

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Assignments• Mixtureofformalexercises,implementation,experimentation,analysis

• “Chooseyourownadventure”componentbasedonyourinterests,e.g.:– exploratorydataanalysis– machinelearning– implementation/scalability– modelanderroranalysis– visualization

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Project• Replicate[partof]apublishedNLPpaper,ordefineyourownproject.

• Theprojectmaybedoneindividuallyorinagroupoftwo.Eachgroupmemberwillreceivethesamegrade.

• Moredetailstocome.

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CollaborationPolicy• Youarewelcometodiscussassignmentswithothersinthecourse,butsolutionsandcodemustbewrittenindividually

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Textbooks

• Allareoptional• SpeechandLanguageProcessing,2nd Ed.

– somechaptersof3rd editionareonline

• TheAnalysisofData,Volume1:Probability– freelyavailableonline

• IntroductiontoInformationRetrieval– freelyavailableonline

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Roadmap• classification• words• lexicalsemantics• languagemodeling• sequencelabeling• syntaxandsyntacticparsing• neuralnetworkmethodsinNLP• semanticcompositionality• semanticparsing• unsupervisedlearning• machinetranslationandotherapplications

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WhyisNLPhard?• ambiguityandvariabilityoflinguisticexpression:– variability:manyformscanmeanthesamething– ambiguity:oneformcanmeanmanythings

• therearemanydifferentkindsofambiguity• eachNLPtaskhastoaddressadistinctsetofkinds

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WordSenseAmbiguity• manywordshavemultiplemeanings

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WordSenseAmbiguity

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credit:A.Zwicky

WordSenseAmbiguity

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credit:A.Zwicky

AttachmentAmbiguity

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MeaningAmbiguity

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• simplestuser-facingNLPapplication• email(spam,priority,categories):

• sentiment:

• topicclassification• others?

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TextClassification

Whatisaclassifier?

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Whatisaclassifier?• afunctionfrominputsx toclassificationlabelsy

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Whatisaclassifier?• afunctionfrominputsx toclassificationlabelsy• onesimpletypeofclassifier:– foranyinputx,assignascoretoeachlabely,parameterizedbyvector :

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Whatisaclassifier?• afunctionfrominputsx toclassificationlabelsy• onesimpletypeofclassifier:– foranyinputx,assignascoretoeachlabely,parameterizedbyvector :

– classifybychoosinghighest-scoringlabel:

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CoursePhilosophy• Fromreadingpapers,onegetstheideathatmachinelearningconceptsaremonolithic,opaqueobjects– e.g.,naïveBayes,logisticregression,SVMs,CRFs,neuralnetworks,LSTMs,etc.

• Nothingisopaque• Everythingcanbedissected,whichrevealsconnections• Thenamesaboveareusefulshorthand,butnotusefulforgainingunderstanding

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CoursePhilosophy• Wewilldrawfrommachinelearning,linguistics,and

algorithms,buttechnicalmaterialwillbe(mostly)self-contained;wewon’tusemanyblackboxes

• Wewillfocusondeclarative(ratherthanprocedural)specifications,becausetheyhighlightconnectionsanddifferences

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Modeling,Inference,Learning

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• Modeling:Howdoweassignascoretoan(x,y)pairusingparameters?

modeling:definescorefunction

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• Inference:Howdoweefficientlysearchoverthespaceofalllabels?

inference:solve_ modeling:definescorefunction

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• Learning:Howdowechoose?

learning:choose_

modeling:definescorefunctioninference:solve_

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• Wewillusethissameparadigmthroughoutthecourse,evenwhentheoutputspacesizeisexponentialinthesizeoftheinputorisunbounded(e.g.,machinetranslation)

learning:choose_

modeling:definescorefunctioninference:solve_

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Notation• We’lluseboldfaceforvectors:

• Individualentrieswillusesubscriptsandnoboldface,e.g.,forentryi:

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Modeling:LinearModels• Scorefunctionislinearin:

• f:featurefunctionvector• :weightvector

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Modeling:LinearModels• Scorefunctionislinearin:

• f:featurefunctionvector• :weightvector• Howdowedefinef?

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DefiningFeatures• ThisisalargepartofNLP• Last20years:featureengineering• Last2years:representationlearning

• Inthiscourse,wewilldoboth• Learningrepresentationsdoesn’tmeanthatwedon’thavetolookatthedataortheoutput!

• There’sstillplentyofengineeringrequiredinrepresentationlearning

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DefiningFeatures• ThisisalargepartofNLP• Last20years:featureengineering• Last2years:representationlearning

• Inthiscourse,we’lldoboth• Learningrepresentationsdoesn’tmeanthatwedon’thavetolookatthedataortheoutput!

• There’sstillplentyofengineeringrequiredinrepresentationlearning

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FeatureEngineering• Oftendecriedas“costly,hand-crafted,expensive,domain-specific”,etc.

• Butinpractice,simplefeaturestypicallygivethebulkoftheperformance

• Let’sgetconcrete:howshouldwedefinefeaturesfortextclassification?

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FeatureEngineeringforTextClassification

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isnowavectorbecauseitisasequenceofwords


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let’sconsidersentimentanalysis:


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so,hereisoursentimentclassifierthatusesalinearmodel:

let’sconsidersentimentanalysis:


• Twofeatures:

where

• Whatshouldtheweightsbe?

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• Twofeatures:

where

• Whatshouldtheweightsbe?

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• Twofeatures:

• Let’ssayweset• Onsentencescontaining“great”intheStanfordSentimentTreebanktrainingdata,thiswouldgetusanaccuracyof69%

• But“great’’onlyappearsin83/6911examples

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• Twofeatures:

• Let’ssayweset• Onsentencescontaining“great”intheStanfordSentimentTreebanktrainingdata,thiswouldgetusanaccuracyof69%

• But“great’’onlyappearsin83/6911examples

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variability:manyotherwordscanindicatepositivesentiment

ambiguity:“great”canmeandifferentthingsindifferentcontexts

• Usually,greatindicatespositivesentiment:Themostwondrous lovestoryinyears,itisagreat film.Agreat companionpiecetootherNapoleon films.

• Sometimesnot.Why?Negation:It'snotagreatmonstermovie.Differentsense:There'sagreatdealofcornydialogueandpreposterousmoments.Multiplesentiments: Agreat ensemblecastcan'tliftthisheartfeltenterpriseoutofthefamiliar.

• Andtherearemanyotherwordsthatindicatepositivesentiment

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• Usually,greatindicatespositivesentiment:Themostwondrous lovestoryinyears,itisagreat film.Agreat companionpiecetootherNapoleon films.

• Sometimesnot.Why?Negation:It'snotagreatmonstermovie.Differentsense:There'sagreatdealofcornydialogueandpreposterousmoments.Multiplesentiments: Agreat ensemblecastcan'tliftthisheartfeltenterpriseoutofthefamiliar.

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• Whataboutafeaturelikethefollowing?

• Whatshoulditsweightbe?

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• Whataboutafeaturelikethefollowing?

• Whatshoulditsweightbe?• Doesn’tmatter.• Why?

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TextClassification

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ourlinearsentimentclassifier:

Inference forTextClassification

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inference:solve_

Inference forTextClassification

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inference:solve_

• trivial(loopoverlabels)

TextClassification

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LearningforTextClassification

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learning:choose_

LearningforTextClassification

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learning:choose_

• Therearemanywaystochoose

ExperimentalPractice• inthebeginning,wejusthaddata• firstinnovation:splitintotrainandtest– motivation:simulateconditionsofapplyingsysteminpractice

• but,there’saproblemwiththis…– weneedtoexploreandevaluatemethodologicalchoices

– aftermultipleevaluationsontest,itisnolongerasimulationofreal-worldconditions

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ExperimentalPractice• inthebeginning,wejusthaddata• firstinnovation:splitintotrain andtest– motivation:simulateconditionsofapplyingsysteminpractice



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ExperimentalPractice• weneedtoexplore/evaluatemethodologicalchoices• whatshouldwedo?– someusecrossvalidationontrain,butthisisslowanddoesn’tquitesimulatereal-worldsettings(why?)

• secondinnovation:dividedataintotrain,test,andathirdsetcalleddevelopmentorvalidation– usedevelopment/validationtoevaluatechoices– then,whenreadytowritethepaper,evaluatethebestmodelontest

• arewedoneyet?no!there’sstillaproblem

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• secondinnovation:dividedataintotrain,test,andathirdsetcalleddevelopment (dev)orvalidation(val)– usedev/val toevaluatechoices– then,whenreadytowritethepaper,evaluatethebestmodelontest

• arewedoneyet?no!there’sstillaproblem:– overfitting todev/val

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ExperimentalPractice• bestpractice:splitdataintotrain,development(dev),developmenttest(devtest),andtest– trainmodelontrain,tunehyperparameter valuesondev,dopreliminarytestingondevtest,dofinaltestingontestasingletimewhenwritingthepaper

– Evenbettertohaveevenmoretestsets!test1,test2,etc.

• experimentalcredibilityisahugecomponentofdoingusefulresearch

• whenyoupublisharesult,ithadbetterbereplicablewithouttuninganythingontest

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Don’tCheat!

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ttic 31190: natural language...

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