Table Of Content

Language,Cognition, andComputational Models Howdoinfantslearnalanguage?Whyandhowdolanguagesevolve?Howdoweunder- standasentence?Thisbookexploresthesequestionsusingrecentcomputationalmod- els that shed new light on issues related to language and cognition. The chapters in this collection propose original analyses of specific problems and develop computa- tionalmodelsthathavebeentestedandevaluatedonrealdata.Featuringcontributions from a diverse group of experts, this interdisciplinary book bridges the gap between natural language processing and cognitive sciences. It is divided into three sections, focusingrespectivelyonmodelsofneuralandcognitiveprocessing,data-drivenmeth- ods,andsocialissuesinlanguageevolution.Thisbookwillbeusefultoanyresearcher andadvancedstudentinterestedintheanalysisofthelinksbetweenthebrainandthe languagefaculty. THIERRY POIBEAUisDirectorofResearchatCNRSandheadoftheLaTTiCElabora- toryinParis,France.HeisalsoanaffiliatedlecturerattheDepartmentofTheoretical andAppliedLinguistics(DTAL)oftheUniversityofCambridge.Heworksonnatural languageprocessing(NLP),inparticularoninformationextraction,questionanswering, semanticzoning,knowledgeacquisitionfromtext,andnamedentitytagging. ALINE VILLAVICENCIO isaffiliatedwiththeInstituteofInformatics,FederalUniver- sity of Rio Grande do Sul in Brazil, and with the School of Computer Science and ElectronicEngineering,EssexUniversity,UK.SheisafellowofCNPq(Brazil).Her researchinterestsinnaturallanguageprocessingareincomputationalmodelsofacqui- sition of linguistic information from data, distributional semantic models, multiword expression,andapplicationsliketextsimplificationandquestionanswering. StudiesinNaturalLanguageProcessing VolumesintheSNLPseriesprovidecomprehensivesurveysofcurrentresearch topicsandapplicationsinthefieldofnaturallanguageprocessing(NLP)that shed light on language technology, language cognition, language and soci- ety,andlinguistics.Theincreasedavailabilityoflanguagecorporaanddigital media,aswellasadvancesincomputertechnologyanddatasciences,hasled toimportantnewfindingsinthefield.Widespreadapplicationsincludevoice- activatedinterfaces,translation,searchengineoptimization,andaffectivecom- puting. NLP also has applications in areas such as knowledge engineering, languagelearning,digitalhumanities,corpuslinguistics,andtextualanalysis. Thesevolumeswillbeofinteresttoresearchersandgraduatestudentsworking inNLPandotherfieldsrelatedtotheprocessingoflanguageandknowledge. ChiefEditor: Chu-RenHuang–TheHongKongPolytechnicUniversity, DepartmentofChineseandBilingualStudies AssociateEditor: QiSu–PekingUniversity,SchoolofForeignLanguages EditorialBoard: StevenBird–UniversityofMelbourne,DepartmentofComputing andInformationSystems FrancisBond–NanyangTechnologicalUniversity, DivisionofLinguisticandMultilingualStudies AlessandroLenci–UniversitàdiPisa,Dipart.diFilologia, LetteraturaeLinguistica LoriLevin–CarnegieMellonUniversity,Language TechnologiesInstitute MaartendeRijke–UniversityofAmsterdam,InformaticsInstitute NianwenXue–BrandeisUniversity,ComputerScienceDepartment Language, Cognition, and Computational Models Editedby ThierryPoibeau CNRS,Paris,France AlineVillavicencio UniversidadeFederaldoRioGrandedoSul,BrazilandUniversityofEssex,UK UniversityPrintingHouse,CambridgeCB28BS,UnitedKingdom OneLibertyPlaza,20thFloor,NewYork,NY10006,USA 477WilliamstownRoad,PortMelbourne,VIC3207,Australia 314–321,3rdFloor,Plot3,SplendorForum,JasolaDistrictCentre, NewDelhi-110025,India 79AnsonRoad,#06-04/06,Singapore079906 CambridgeUniversityPressispartoftheUniversityofCambridge. ItfurtherstheUniversity’smissionbydisseminatingknowledgeinthepursuitof education,learning,andresearchatthehighestinternationallevelsofexcellence. www.cambridge.org Informationonthistitle:www.cambridge.org/9781107162228 DOI:10.1017/9781316676974 ©CambridgeUniversityPress2017 Thispublicationisincopyright.Subjecttostatutoryexception andtotheprovisionsofrelevantcollectivelicensingagreements, noreproductionofanypartmaytakeplacewithoutthewritten permissionofCambridgeUniversityPress. Firstpublished2017 PrintedintheUnitedStatesofAmericabySheridanBooks,Inc. AcataloguerecordforthispublicationisavailablefromtheBritishLibrary ISBN978-1-107-16222-8Hardback CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyof URLsforexternalorthird-partyInternetWebsitesreferredtointhispublication anddoesnotguaranteethatanycontentonsuchWebsitesis,orwillremain, accurateorappropriate. Contents Figures pageix Tables xi Contributors xv PartI AboutThisBook 1 Introduction:CognitiveIssuesinNaturalLanguage Processing 3 thierry poibeauandalinevillavicencio 1.1 OntheRelationshipsbetweenNaturalLanguageProcessingand CognitiveSciences 3 1.2 RecentIssuesinCognitiveAspectsofLanguageModeling 8 1.3 ContentandStructureoftheBook 14 PartII ModelsofNeuralandCognitiveProcessing 2 LightandDeepParsing:ACognitiveModelofSentence Processing 27 philippe blache 2.1 Introduction 27 2.2 AnInterdisciplinaryViewofLanguageProcessing 28 2.3 TheTheoreticalFramework:PropertyGrammars 39 2.4 Chunks,Constructions,andProperties 42 2.5 TheHybridArchitecture 46 2.6 Conclusion 49 3 DecodingLanguagefromtheBrain 53 brianmurphy,leilawehbe,andalonafyshe 3.1 Introduction 53 3.2 GroundingLanguageArchitectureintheBrain 55 3.3 DecodingWordsintheBrain 62 3.4 PhrasesintheBrain 66 3.5 StoriesintheBrain 68 3.6 Summary 72 v vi Contents 4 GraphTheoryAppliedtoSpeech:InsightsonCognitive DeficitDiagnosisandDreamResearch 81 natália bezerramota,maurocopelli,and sidarta ribeiro 4.1 Introduction 82 4.2 SemanticAnalysisfortheDiagnosisofPsychosis 84 4.3 WhatIsaSpeechGraph? 85 4.4 SpeechGraphsasaStrategytoQuantifySymptomson Psychosis 88 4.5 DifferencesinSpeechGraphsduetoContent(waking×dream reports) 92 4.6 SpeechGraphsAppliedtoDementia 94 4.7 FuturePerspectives 96 PartIII DataDrivenModels 5 PuttingLinguisticsBackintoComputationalLinguistics 101 martinkay 5.1 ExplicitandImplicitInformation 101 5.2 Features 108 5.3 LinguisticComputationandComputationalLinguistics 114 5.4 Conclusion 116 6 ADistributionalModelofVerb-SpecificSemanticRoles Inferences 118 gianlucae.lebaniandalessandro lenci 6.1 RepresentingandAcquiringThematicRoles 119 6.2 CharacterizingtheSemanticContentofVerbProto-roles 122 6.3 ADistributionalModelofThematicRoles 130 6.4 ExperimentswithOurNeo-DavidsonianModel 139 6.5 Conclusion 148 7 NativeLanguageIdentificationonEFCAMDAT 159 xiaojiang,yanhuang,yufanguo,jeroen geertzen,theodoraalexopoulou, linsun,and annakorhonen 7.1 Introduction 159 7.2 Data 165 7.3 Methods 168 7.4 Results 172 7.5 Conclusion 181 8 EvaluatingLanguageAcquisitionModels:AUtility-Based LookatBayesianSegmentation 185 lisapearlandlawrence phillips 8.1 Introduction 185 8.2 EarlySpeechSegmentation 187 Contents vii 8.3 ABayesianSegmentationStrategy 190 8.4 HowWellDoesThisWorkCross-Linguistically? 196 8.5 HowUsefulAretheUnits? 207 8.6 ClosingThoughts 219 PartIV SocialandLanguageEvolution 9 SocialEvolutionofPublicLanguages:BetweenRousseau’s EdenandHobbes’Leviathan 227 annereboul 9.1 Introduction 227 9.2 IsLanguageaCommunicationSystemintheStrongSense? 228 9.3 WhatistheProperSocialAccountfortheExaptationofLanguage forCommunication? 233 9.4 Conclusion 250 10 GeneticBiasesinLanguage:ComputerModelsand ExperimentalApproaches 256 rickjanssen anddandediu 10.1 Introduction 256 10.2 ComputerModelsofCulturalEvolution 262 10.3 CulturalFeedback 278 10.4 Conclusion 281 11 TransparencyversusProcessingEfficiency:ACaseStudy onGermanDeclension 289 remivantrijp 11.1 Introduction 289 11.2 GermanDeclension:NotasAwfulasItSeems 290 11.3 EvaluatingtheEfficiencyofSyncretism 300 11.4 DiscussionandConclusions 314 Index 319 Figures 2.1 Aclassicalgenerativearchitectureoflanguageprocessing page29 2.2 OutputoftheStanfordparser 30 2.3 ThemainERPcomponentsinlanguageprocessing 31 2.4 Generalorganizationofthethree-phasesmodel[Friederici, 2011] 32 2.5 Thedifferencesbetweenconditionsinidiomaticcontext(COR, correctsentence;REL,softviolation;UNREL,hardviolation) 35 2.6 Parafovealvision.Extractingfeaturesfromthesurrounding words 36 2.7 EarlyEEGeffectsofsyntacticviolation(mismatchnegativity) 37 3.1 Anoverviewofthelocationandtimingoflanguageprocessing (forsentences)inthebrain 56 3.2 AnexampleMEGrecordingaveragedovertwentyrepetitions ofapersonreadingthewordbear 58 3.3 AnfMRIimageaveragedoversixrepetitionofaperson readingthewordbear 59 3.4 SeveralslicesfromfMRIimagesshowingthelearned proportionofbrainactivationthatcanbeassociatedwitha particularverbfromthesetoftwenty-fiveverbsusedin Mitchelletal.(2008) 64 3.5 Thepredicted(toprow)andobserved(bottomrow)brain activationforaparticularpersonwhilereadingtheword “celery”or“airplane”(leftandrightcolumns,respectively) 65 3.6 Storyreadingbrainmap,adaptedfromWehbeetal.(2014b) 70 3.7 Time-lineofwordintegrationacrosstheMEGhelmet 71 4.1 Examplesofspeechgraphsfromdreamreportsof schizophrenic,bipolar,andcontrolsubjects 86 4.2 ExamplesofSpeechGraphAttributes 87 4.3 LinearcorrelationbetweenSGAandwordcount(WC) 89 4.4 Representativespeechgraphsextractedfromdreamreports fromaschizophrenic,abipolarandacontrolsubject 91 ix