计算语言学协会会刊, 卷. 4, PP. 31–45, 2016. 动作编辑器: Tim Baldwin.

计算语言学协会会刊, 卷. 4, PP. 31–45, 2016. 动作编辑器: Tim Baldwin.
提交批次: 12/2015; 修改批次: 2/2016; 已发表 2/2016.

2016 计算语言学协会. 根据 CC-BY 分发 4.0 执照.

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(西德:13)

ABayesianModelofDiachronicMeaningChangeLeaFrermannandMirellaLapataInstituteforLanguage,CognitionandComputationSchoolofInformatics,UniversityofEdinburgh10CrichtonStreet,EdinburghEH89ABl.frermann@ed.ac.uk,mlap@inf.ed.ac.ukAbstractWordmeaningschangeovertimeandanau-tomatedprocedureforextractingthisinfor-mationfromtextwouldbeusefulforhistor-icalexploratorystudies,informationretrievalorquestionanswering.Wepresentady-namicBayesianmodelofdiachronicmeaningchange,whichinferstemporalwordrepresen-tationsasasetofsensesandtheirprevalence.Unlikepreviouswork,weexplicitlymodellanguagechangeasasmooth,gradualpro-cess.Weexperimentallyshowthatthismodel-ingdecisionisbeneﬁcial:ourmodelperformscompetitivelyonmeaningchangedetectiontaskswhilstinducingdiscerniblewordsensesandtheirdevelopmentovertime.ApplicationofourmodeltotheSemEval-2015temporalclassiﬁcationbenchmarkdatasetsfurtherre-vealsthatitperformsonparwithhighlyop-timizedtask-speciﬁcsystems.1IntroductionLanguageisadynamicsystem,constantlyevolv-ingandadaptingtotheneedsofitsusersandtheirenvironment(Aitchison,2001).Wordsinalllan-guagesnaturallyexhibitarangeofsenseswhosedis-tributionorprevalencevariesaccordingtothegenreandregisterofthediscourseaswellasitshistoricalcontext.Asanexample,considerthewordcutewhichaccordingtotheOxfordEnglishDictionary(OED,Stevenson2010)ﬁrstappearedintheearly18thcenturyandoriginallymeantcleverorkeen-witted.1Bythelate19thcenturycutewasusedin1Throughoutthispaperwedenotewordsintruetype,theirsensesinitalics,andsense-speciﬁccontextwordsas{列表}.thesamesenseascunning.Todayitmostlyreferstoobjectsorpeopleperceivedasattractive,prettyorsweet.Anotherexampleisthewordmousewhichinitiallywasonlyusedintherodentsense.TheOEDdatesthecomputerpointingdevicesenseofmouseto1965.Thelattersensehasbecomepar-ticularlydominantinrecentdecadesduetotheever-increasinguseofcomputertechnology.Thearrivaloflarge-scalecollectionsofhistorictexts(戴维斯,2010)andonlinelibrariessuchastheInternetArchiveandGoogleBookshavegreatlyfacilitatedcomputationalinvestigationsoflanguagechange.Theabilitytoautomaticallydetecthowthemeaningofwordsevolvesovertimeispotentiallyofsigniﬁcantvaluetolexicographicandlinguisticresearchbutalsotorealworldapplications.Time-speciﬁcknowledgewouldpresumablyrenderwordmeaningrepresentationsmoreaccurate,andbeneﬁtseveraldownstreamtaskswheresemanticinforma-tioniscrucial.Examplesincludeinformationre-trievalandquestionanswering,wheretime-relatedinformationcouldincreasetheprecisionofquerydisambiguationanddocumentretrieval(e.g.,byre-turningdocumentswithnewlycreatedsensesorﬁl-teringoutdocumentswithobsoletesenses).InthispaperwepresentadynamicBayesianmodelofdiachronicmeaningchange.Wordmean-ingismodeledasasetofsenses,whicharetrackedoverasequenceofcontiguoustimeintervals.Weinfertemporalmeaningrepresentations,consistingofaword’ssenses(asaprobabilitydistributionoverwords)andtheirrelativeprevalence.Ourmodelisthusabletodetectthatmousehadonesenseuntilthemid-20thcentury(characterizedbywordssuchas{cheese,tail,rat})andsubsequentlyacquireda

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secondsenserelatingtocomputerdevice.More-over,itinferssubtlechangeswithinasinglesense.Forinstance,inthe1970sthewords{电缆,ball,mousepad}weretypicalforthecomputerdevicesense,whereasnowadaystheterms{optical,laser,usb}aremoretypical.Contrarytopreviouswork(Mitraetal.,2014;MihalceaandNastase,2012;Gu-lordavaandBaroni,2011)wheretemporalrepresen-tationsarelearntinisolation,ourmodelassumesthatadjacentrepresentationsareco-dependent,thuscapturingthenatureofmeaningchangebeingfun-damentallysmoothandgradual(麦克马洪,1994).Thisalsoservesasaformofsmoothing:temporallyneighboringrepresentationsinﬂuenceeachotheriftheavailabledataissparse.Experimentalevaluationshowsthatourmodel(A)inducestemporalrepresentationswhichreﬂectwordsensesandtheirdevelopmentovertime,(乙)isabletodetectmeaningchangebetweentwotimepe-riods,和(C)isexpressiveenoughtoobtainusefulfeaturesforidentifyingthetimeintervalinwhichapieceoftextwaswritten.Overall,ourresultsindi-catethatanexplicitmodeloftemporaldynamicsisadvantageousfortrackingmeaningchange.Com-parisonsacrossevaluationsandagainstavarietyofrelatedsystemsshowthatdespitenotbeingdesignedwithanyparticulartaskinmind,ourmodelperformscompetitivelyacrosstheboard.2RelatedWorkMostworkondiachroniclanguagechangehasfo-cusedondetectingwhetherandtowhatextentaword’smeaningchanged(e.g.,betweentwoepochs)withoutidentifyingwordsensesandhowthesevaryovertime.Avarietyofmethodshavebeenappliedtothetaskrangingfromtheuseofstatisticaltestsinordertodetectsigniﬁcantchangesinthedistributionoftermsfromtwotimeperiods(PopescuandStrap-parava,2013;CookandStevenson,2010),totrain-ingdistributionalsimilaritymodelsontimeslices(GulordavaandBaroni,2011;Sagietal.,2009),andneurallanguagemodels(Kimetal.,2014;Kulkarnietal.,2015).Otherwork(MihalceaandNastase,2012)takesasupervisedlearningapproachandpre-dictsthetimeperiodtowhichawordbelongsgivenitssurroundingcontext.Bayesianmodelshavebeenpreviouslydevelopedforvarioustasksinlexicalsemantics(BrodyandLa-pata,2009;´OS´eaghdha,2010;Ritteretal.,2010)andwordmeaningchangedetectionisnoexception.Usingtechniquesfromnon-parametrictopicmodel-ing,Lauetal.(2012)inducewordsenses(aka.top-ics)foragiventargetwordovertwotimeperiods.Novelsensesarethenaredetectedbasedonthediscrepancybetweensensedistributionsinthetwoperiods.Follow-upwork(Cooketal.,2014;Lauetal.,2014)furtherexploresmethodsforhowtobestmeasurethissensediscrepancy.Ratherthaninfer-ringwordsenses,WijayaandYeniterzi(2011)useaTopics-over-Timemodelandk-meansclusteringtoidentifytheperiodsduringwhichselectedwordsmovefromonetopictoanother.Anon-BayesianapproachisputforwardinMi-traetal.(2014,2015)whoadoptagraph-basedframeworkforrepresentingwordmeaning(seeTah-masebietal.(2011)forasimilarearlierproposal).Inthismodelwordscorrespondtonodesinase-manticnetworkandedgesaredrawnbetweenwordssharingcontextualfeatures(extractedfromadepen-dencyparser).Agraphisconstructedforeachtimeinterval,andnodesareclusteredintosenseswithChineseWhispers(Biemann,2006),arandomizedgraphclusteringalgorithm.Bycomparingthein-ducedsensesforeachtimesliceandobservinginter-clusterdifferences,theirmethodcandetectwhethersensesemergeordisappear.Ourworkdrawsideasfromdynamictopicmod-eling(BleiandLafferty,2006乙)wheretheevolu-tionoftopicsismodeledvia(smooth)changesintheirassociateddistributionsoverthevocabulary.Althoughthedynamiccomponentofourmodeliscloselyrelatedtopreviousworkinthisarea(Mimnoetal.,2008),ourmodelisspeciﬁcallyconstructedforcapturingsenseratherthantopicchange.Ourap-proachisconceptuallysimilartoLauetal.(2012).Wealsolearnajointsenserepresentationformulti-pletimeslices.However,inourcasethenumberoftimeslicesinnotrestrictedtotwoandweexplicitlymodeltemporaldynamics.LikeMitraetal.(2014,2015),wemodelhowsenseschangeovertime.Inourmodel,temporalrepresentationsarenotinde-pendent,butinﬂuencedbytheirtemporalneighbors,encouragingsmoothchangeovertime.Wethereforeinduceaglobalandconsistentsetoftemporalrepre-sentationsforeachword.Ourmodelisknowledge-lean(itdoesnotmakeuseofaparser)andlanguage

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独立的(allthatisneededisatime-stampedcorpusandtoolsforbasicpre-processing).ContrarytoMitraetal.(2014,2015),wedonottreatthetasksofinferringasemanticrepresentationforwordsandtheirsensesastwoseparateprocesses.Evaluationofmodelswhichdetectmeaningchangeisfraughtwithdifﬁculties.Thereisnostan-dardsetofwordswhichhaveundergonemeaningchangeorbenchmarkcorpuswhichrepresentsava-rietyoftimeintervalsandgenres,andisthematicallyconsistent.Previousworkhasgenerallyfocusedonafewhand-selectedwordsandmodelswereevalu-atedqualitativelybyinspectingtheiroutput,ortheextenttowhichtheycandetectmeaningchangesfromtwotimeperiods.Forexample,Cooketal.(2014)manuallyidentify13targetwordswhichun-dergomeaningchangeinafocuscorpuswithre-specttoareferencecorpus(bothnewstext).Theythenassesshowtheirmodelsfareatlearningsensedifferencesforthesetargetscomparedtodistractorswhichdidnotundergomeaningchange.Theyalsounderlinetheimportanceofusingthematicallycom-parablereferenceandfocuscorporatoavoidspuri-ousdifferencesinwordrepresentations.Inthisworkweevaluateourmodel’sabilitytodetectandquantifymeaningchangeacrossseveraltimeintervals(notjusttwo).Insteadofrelyingonafewhand-selectedtargetwords,weuselargersetssampledfromourlearningcorpusorfoundtoundergomeaningchangeinajudgmentelicitationstudy(GulordavaandBaroni,2011).Inaddition,weadopttheevaluationparadigmofMitraetal.(2014)andvalidateourﬁndingsagainstWordNet.Finally,weapplyourmodeltotherecentlyes-tablishedSemEval-2015diachronictextevaluationsubtasks(PopescuandStrapparava,2015).Inordertopresentaconsistentsetofexperiments,weuseourowncorpusthroughoutwhichcoversawiderrangeoftimeintervalsandiscompiledfromavarietyofgenresandsourcesandisthusthematicallycoher-ent(seeSection4fordetails).Whereverpossible,wecompareagainstpriorart,withthecaveatthattheuseofadifferentunderlyingcorpusunavoidablyinﬂuencestheobtainedsemanticrepresentations.3ABayesianModelofSenseChangeInthissectionweintroduceSCAN,ourdynamicBayesianmodelofSenseChANge.SCANcaptureshowaword’ssensesevolveovertime(e.g.,whethernewsensesemerge),whethersomesensesbecomemoreorlessprevalent,aswellasphenomenaper-tainingtoindividualsensessuchasmeaningexten-sion,转移,ormodiﬁcation.Weassumethattimeisdiscrete,dividedintocontiguousintervals.Givenaword,ourmodelinfersitssensesforeachtimein-tervalandtheirprobability.Itcapturesthegradualnatureofmeaningchangeexplicitly,throughdepen-denciesbetweentemporallyadjacentmeaningrep-resentations.Sensesthemselvesareexpressedasaprobabilitydistributionoverwords,whichcanalsochangeovertime.3.1ModelDescriptionWecreateaSCANmodelforeachtargetwordc.Theinputtothemodelisacorpusofshorttextsnippets,eachconsistingofamentionofthetargetwordcanditslocalcontextw(inourexperimentsthisisasym-metriccontextwindowof±5words).Eachsnip-petisannotatedwithitsyearoforigin.Themodelisparametrizedwithregardtothenumberofsensesk∈[1…K]ofthetargetwordc,andthelengthoftimeintervals∆Twhichmightbeﬁnelyorcoarselydeﬁned(e.g.,spanningayearoradecade).Weconﬂatealldocumentsoriginatingfromthesametimeintervalt∈[1…时间]andinferatempo-ralrepresentationofthetargetwordperinterval.Atemporalmeaningrepresentationfortimetis(A)aK-dimensionalmultinomialdistributionoverwordsensesφtand(乙)aV-dimensionaldistributionoverthevocabularyψt,kforeachwordsensek.Inad-dition,ourmodelinfersaprecisionparameterκφ,whichcontrolstheextenttowhichsenseprevalencechangesforwordcovertime(seeSection3.2fordetailsonhowwemodeltemporaldynamics).Weplaceindividuallogisticnormalpriors(BleiandLafferty,2006A)onourmultinomialsensedis-tributionsφandsense-worddistributionsψk.Adrawfromthelogisticnormaldistributioncon-sistsof(A)adrawofann-dimensionalrandomvectorxfromthemultivariatenormaldistributionparametrizedbyann-dimensionalmeanvectorµandan×nvariance-covariancematrixΣ,x∼N(X|µ,Σ);和(乙)amappingofthedrawnparam-eterstothesimplexthroughthelogistictransforma-tionφn=exp(xn)/Pn0exp(xn0),whichensuresadrawofvalidmultinomialparameters.Thenormaldistributionsareparametrizedtoencouragesmooth

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wzzwzwφt−1φtφt+1κφa,bψt−1ψtψt+1κψIDt−1IDtIDt+1KDrawκφ∼Gamma(A,乙)fortimeintervalt=1..TdoDrawsensedistributionφt|φ−t,κφ∼N(12(φt−1+φt+1),κφ)forsensek=1..KdoDrawworddistributionψt,k|ψ−t,κψ∼N(12(ψt−1,k+ψt+1,k),κψ)fordocumentd=1..DdoDrawsensezd∼Mult(φt)forcontextpositioni=1..IdoDrawwordwd,i∼Mult(ψt,zd)Figure1:左边:platediagramforthedynamicsensemodelforthreetimesteps{t−1,t,t+1}.Constantparametersareshownasdashednodes,latentvariablesasclearnodes,andobservedvariablesasgraynodes.Right:thecorrespondinggenerativestory.changeinmultinomialparameters,overtime(seeSection3.2fordetails),andtheextentofchangeiscontrolledthroughaprecisionparameterκ.Welearnthevalueofκφduringinference,whichal-lowsustomodeltheextentoftemporalchangeinsenseprevalenceindividuallyforeachtargetword.WedrawκφfromaconjugateGammaprior.Wedonotinferthesense-wordprecisionparameterκψonallψk.Instead,weﬁxitatahighvalue,trig-geringlittlevariationofworddistributionswithinsenses.Thisleadstosensesbeingthematicallyco-herentovertime.Wenowdescribethegenerativestoryofourmodel,whichisdepictedinFigure1(正确的),along-sideitsplatediagramrepresentation(左边).第一的,wedrawthesenseprecisionparameterκφfromaGammaprior.Foreachtimeintervaltwedraw(A)amultinomialdistributionoversensesφtfromalo-gisticnormalprior;和(乙)amultinomialdistribu-tionoverthevocabularyψt,kforeachsensek,fromanotherlogisticnormalprior.Next,wegeneratetime-speciﬁctextsnippets.Foreachsnippetd,weﬁrstobservethetimeintervalt,anddrawasensezdfromMult(φt).最后,wegenerateIcontextwordswd,iindependentlyfromMult(ψt,zd).3.2BackgroundoniGMRFsLetφ={φ1…φT}denoteaT-dimensionalrandomvector,whereeachφtmightforexamplecorrespondtoasenseprobabilityattimet.Wedeﬁneapriorwhichencouragessmoothchangeofparametersatneighboringtimes,intermsofaﬁrstorderrandomwalkontheline(graphicallyshowninFigure2,andthechainsofφandψinFigure1(左边)).Speciﬁcally,wedeﬁnethispriorasanintrinsicGaussianMarkovRandomField(iGMRF;RueandHeld2005),whichallowsustomodelthechangeofadjacentparame-tersasdrawnfromanormaldistribution,例如:∆φt∼N(0,κ−1).(1)TheiGMRFisdeﬁnedwithrespecttothegraphinFigure2;itissparselyconnectedwithonlyﬁrst-orderdependencieswhichallowsforefﬁcientin-ference.Asecondfeature,whichmakesiGMRFspopularaspriorsinBayesianmodeling,isthefactthattheycanbedeﬁnedpurelyintermsofthelo-calchangesbetweendependent(i.e.,adjacent)vari-ables,withouttheneedtospecifyanoverallmeanofthemodel.Thefullconditionalsexplicitlycap-turetheseintuitions:φt|φ−t,κ∼N(西德:16)12(φt−1+φt+1),12κ(西德:17),(2)for1l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 35 φ1φt−1φtφt+1φTFigure2:AlinearchainiGMRF.inourcase:howtightlycoupledaretemporallyad-jacentmeaningrepresentationsofawordc?Wees-timatetheprecisionparameterκφduringinference.Thisallowsustoﬂexiblycapturesensevariationovertimeindividuallyforeachtargetword.Foradetailedintroductionto(我)GMRFswerefertheinterestedreadertoRueandHeld(2005).ForanapplicationofiGMRFstotopicmodelsseeMimnoetal.(2008).3.3InferenceWeuseablockedGibbssamplerforapproximatein-ference.Thelogisticnormalpriorisnotconjugatetothemultinomialdistribution.Thismeansthatthestraightforwardparameterupdatesknownforsam-plingstandard,Dirichlet-multinomial,topicmodelsdonotapply.However,sampling-basedmethodsforlogisticnormaltopicmodelshavebeenproposedintheliterature(Mimnoetal.,2008;Chenetal.,2013).Ateachiteration,wesample:(A)document-senseassignments,(乙)multinomialparametersfromthelogisticnormalprior,和(C)thesensepreci-sionparameterfromaGammaprior.Ourblockedsamplerﬁrstiteratesoverallinputtextsnippetsdwithcontextw,andre-samplestheirsenseassign-mentsunderthecurrentmodelparameters{φ}Tand{ψ}K×T,p(zd|w,t,φ,ψ)∝p(西德:16)zd|t(西德:17)p(西德:16)w|t,zd(西德:17)=φtzdYw∈wψt,zdw(3)下一个,were-sampleparameters{φ}Tand{ψ}K×Tfromthelogisticnormalprior,giventhecurrentsenseassignments.WeusetheauxiliaryvariablemethodproposedinMimnoetal.(2008)(seealsoGroenewaldandMokgatlhe(2005)).直观地,eachindividualparameter(e.g.,sensek’sprevalenceattimet,φtk)is‘shifted’withinaweightedregionwhichisboundedbythenumberoftimessensekwasobservedattimet.Theweightsoftheregionaredeterminedbytheprior,inourcasethenormaldistributionsdeﬁnedbytheiGMRF,whichensureCorpusyearscovered#wordsCOHA1810–2009142,587,656DTE1700–2010124,771CLMET3.01710–18104,531,505Table1:Sizeandcoverageofourthreetrainingcorpora(afterpre-processing).aninﬂuenceoftemporalneighborsφt−1kandφt+1konthenewparametervalueφtk,andsmoothtempo-ralvariationasdesired.Thesameprocedureappliestoeachwordparameterundereach{时间,感觉}ψt,kw(seeMimnoetal.2008foramoredetailedde-scriptionofthesampler).最后,weperiodicallyre-samplethesenseprecisionparameterκφfromitsconjugateGammaprior.4TheDATECorpusBeforepresentingourevaluationwedescribethecorpususedasabasisfortheexperimentsper-formedinthiswork.WeappliedourmodeltoaDiAchronicTExtcorpus(DATE)whichcollatesdocumentsspanningyears1700–2010fromthreesources:(A)theCOHAcorpus2(戴维斯,2010),alargecollectionoftextsfromvariousgenrescover-ingtheyears1810–2010;(乙)thetrainingdatapro-videdbytheDTEtask3organizers(seeSection8);和(C)theportionoftheCLMET3.04corpus(Dilleretal.,2011)correspondingtotheperiod1710–1810(whichisnotcoveredbytheCOHAcorpusandthusunderrepresentedinourtrainingdata).CLMET3.0containstextsrepresentativeofarangeofgenresin-cludingnarrativeﬁction,drama,字母,andwascol-lectedfromvariousonlinearchives.Table1pro-videsdetailsonthesizeofourcorpus.Documentswereclusteredbytheiryearofpub-licationasindicatedintheoriginalcorpora.IntheCLMET3.0corpus,occasionallyarangeofyearswouldbeprovided.Inthiscaseweusedtheﬁ-nalyearoftherange.Wetokenized,lemmatized,andpartofspeechtaggedDATEusingtheNLTK(Birdetal.,2009).Weremovedstopwordsandfunc-tionwords.Afterpreprocessing,weextractedtarget2http://corpus.byu.edu/coha/3http://alt.qcri.org/semeval2015/task7/index.php?id=data-and-tools4http://www.kuleuven.be/˜u0044428/clmet3_0.htm l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 36 word-speciﬁcinputcorporaforourmodels.Theseconsistedofmentionsofatargetcanditssurround-ingcontext,asymmetricwindowof±5words.5Experiment1:TemporalDynamicsAsdiscussedearlierourmodeldepartsfromprevi-ousapproaches(e.g.,Mitraetal.2014)inthatitlearnsgloballyconsistenttemporalrepresentationsforeachword.Inordertoassesswhethertemporaldependenciesareindeedbeneﬁcial,weimplementedastripped-downversionofourmodel(SCAN-NOT)whichdoesnothaveanytemporaldependenciesbe-tweenindividualtimesteps(i.e.,withoutthechainiGMRFpriors).Wordmeaningisstillrepresentedassensesandsenseprevalenceismodeledasadis-tributionoversensesforeachtimeinterval.How-ever,timeintervalsarenowindependent.InferenceworksasdescribedinSection3.3,withouthavingtolearntheκprecisionparameters.ModelsandParametersWecomparedthetwomodelsintermsoftheirpredictivepower.WesplittheDATEcorpusintoatrainingperiod{d1...dt}oftimeslices1throughtandcomputedthelike-lihoodp(dt+1|φt,ψt)ofthedataattesttimeslicet+1,undertheparametersinferredfortheprevioustimeslice.Thetimeslicesizewassetto∆T=20years.WesetthenumberofsensestoK=8,thewordprecisionparameterκψ=10,ahighvaluewhichenforcesindividualsensestore-mainthematicallyconsistentacrosstime.Wesettheinitialsenseprecisionparameterκφ=4,andtheGammaparametersa=7andb=3.Thesepa-rameterswereoptimizedonceonthedevelopmentdatausedforthetask-basedevaluationdiscussedinSection8.Unlessotherwisespeciﬁedallex-perimentsusethesevalues.Noparametersweretunedonthetestsetforanytask.Inallexper-imentswerantheGibbssamplerfor1,000itera-tions,andresampledκφafterevery50iterations,startingfromiteration150.Weusedtheﬁnalstateofthesamplerthroughout.Werandomlyselected50mid-frequencytargetconceptsfromalargersetoftargetconceptsdescribedinSection8.Predictiveloglikelihoodscoreswereaveragedacrossconceptsandwerecalculatedastheaverageunder10param-etersamples{φt,ψt}fromthetrainedmodels.1920-391940-591960-791980-99−1,7−1,65·105predictedtimeperiodloglikelihoodSCANSCAN-NOTFigure3:PredictiveloglikelihoodofSCANandaver-sionwithouttemporaldependencies(SCAN-NOT)acrossvarioustesttimeperiods.ResultsFigure3displayspredictiveloglikelihoodscoresforfourtesttimeintervals.SCANoutper-formsitsstripped-downversionthroughout(higherisbetter).SincetherepresentationslearntbySCANareinﬂuenced(orsmoothed)byneighboringrepre-sentations,theyoverﬁtspeciﬁctimeintervalslesswhichleadstobetterpredictiveperformance.Fig-ure4furthershowshowSCANmodelsmeaningchangeforthewordsband,力量,transportandbank.Thesensedistributionsovertimeareshownasasequenceofstackedhistograms,sensesthemselvesarecolor-coded(andenumerated)以下,inthesameorderasinthehistograms.Eachsensekisillustratedasthe10wordswassignedthehighestposteriorprobability,marginalizingoverthetime-speciﬁcrepresentationsp(w|k)=Ptψt,kw.Wordsrepresentativeofprevalentsensesarehighlightedinboldface.Figure4(topleft)demonstratesthatthemodelisabletocapturevarioussensesofthewordband,suchasstripusedforbinding(yellowbars/number3intheﬁgure)ormusicalband(grey/1,orange/7).Ourmodelpredictsanincreaseinprevalenceoverthemodeledtimeperiodforbothsenses.Thisiscor-roboratedbytheOEDwhichprovidesthemajorityofreferencesforthebindingstripsenseforthe20thcenturyanddatesthemusicalbandsenseto1812.Inadditionasocialbandsense(violet/6,darkgreen/8;inthesenseofbonding)emerges,whichispresentacrosstimeslices.Thesensecoloredbrown/2referstotheBritishBand,agroupofnativeAmericansinvolvedintheBlackHawkWarin1832,andthemodelindeedindicatesaprevalenceofthissensearoundthistime(seebars1800–1840intheﬁgure).Forthewordpower(Figure4(topright)), l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 37 1700172017401760178018001820184018601880190019201940196019802000band8 band play people time little call father day love boy7 play band music time country day march military frequency jazz6 little hand play land love time night speak strong name5 little soldier leader time land arm hand country war indian4 music play dance band hear time little evening stand house3 black white hat broad gold wear hair band head rubber2 indian little day horse time people meet chief leave war1 play music hand hear sound march street air look strike1700172017401760178018001820184018601880190019201940196019802000power8 power idea god hand mind body life time object nature7 power nation world war country time government sir mean lord6 power time company water force line electric plant day run5 power government law congress executive president legislative constitution4 love power life time woman heart god tell little day3 mind power time life friend woman nature love world reason2 power people law government mind call king time hand nature1 power country government nation war increase world political people europe1700172017401760178018001820184018601880190019201940196019802000transport8 road cost public railway transport rail average service bus time7 ozone epa example section transport air policy region measure caa6 time transport land public ship line water vessel london joy5 air plane ship army day transport land look leave hand4 time road worker union service public system industry air railway3 air international worker plane association united union aircraft line president2 troop ship day land army war send plane supply fleet1 air joy love heart heaven time company eye hand smile1700172017401760178018001820184018601880190019201940196019802000bank8 bank tell cashier teller money day ned president house city7 bank note money deposit credit amount pay species issue bill6 bank money national note government credit united time currency loan5 bank dollar money note national president account director company little4 river day opposite mile bank danube town left country shore3 bank capital company stock rate national president fund city loan2 river water stream foot mile tree stand reach little land1 note bank money time tell leave hard day dollar accountFigure4:Trackingmeaningchangeforthewordsband,力量,transportandbankover20-yeartimeintervalsbetween1700and2010.Eachbarshowstheproportionofeachsense(color-coded)andislabeledwiththestartyearoftherespectivetimeinterval.Sensesareshownasthe10mostprobablewords,andparticularlyrepresentativewordsarehighlightedforillustration.17002010timep(w|s,t)timelinewaterwatercompanycompanypowerpowercompanypowerpowerpowernuclearlinetimepowerforcepowerwatercompanycompanypowercompanyplantnuclearpowerpowerwaterlinecompanytimepowerforceforceforceplantnuclearplantplantwaterpowertimepowerforceforcewatertimeplantelectricelectrictimeutilityforceforceforcelinewatertimeelectricwaterwatertimetimecompanycompanywarwarcompanytimesteamdaydayplantdayforcecompanyutilitytimerundayrunsteamelectriclinetimedaytimedayrunrunpeopleequalhouseelectricdayrunsteamsteamelectricelectricrunutilityelectricenergycarryrunsteamelectricdaypurchaselinesteamrunwaterdaycostcostelectriccompanydayrunplantrunplantrunlinepeopleforcepeoplerunFigure5:Sense-internaltemporaldynamicsfortheenergysenseofthewordpower(violet/6inFigure4).Columnsshowthetenmosthighlyassociatedwordsforeachtimeintervalfortheperiodbetween1700and2010(orderedbydecreasingprobability).Wehighlighthowfourtermscharacteristicofthesensedevelopovertime(看{水,蒸汽,plant,核}intheﬁgure).threesensesemerge:theinstitutionalpower(col-orsgray/1,brown/2,pink/5,orange/7intheﬁgure),mentalpower(yellow/3,lightgreen/4,darkgreen/8),andpowerassupplyofenergy(violet/6).Thelatterisanexampleofa“sensebirth”(Mitraetal.,2014):thesensewashardlypresentbeforethemid-19thcentury.ThisiscorroboratedbytheOEDwhichdatesthesenseto1889,whereastheOEDcontainsreferencestotheremainingsensesforthewholemodeledtimeperiod,aspredictedbyourmodel. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 38 1900-191920-391900-191940-591900-191960-791900-191980-991900-192000-101960-791980-991960-792000-101980-992000-100.20.40.6precisionSCANSCAN-NOTt1t2Figure6:PrecisionresultsfortheSCANandSCAN-NOTmodelsontheWordNet-basednovelsensedetection(Exper-iment2).Resultsareshownforaselectionofreferencetimes(t1)andfocustimes(t2).Similartrendsofmeaningchangeemergefortransport(Figure4bottomleft).Thebot-tomrightplotshowsthesensedevelopmentforthewordbank.Althoughthewell-knownsensesriverbank(brown/2,lightgreen/4)andmonetaryinstitu-tion(休息)emergeclearly,theoverallsensepatternappearscomparativelystableacrossintervalsindi-catingthatthemeaningofthewordhasnotchangedmuchovertime.Besidestrackingsenseprevalenceovertime,ourmodelcanalsodetectchangeswithinindividualsenses.Becauseweareinterestedintrackingse-manticallystablesenses,weﬁxedtheprecisionpa-rameterκψtoahighvalue,todiscouragetoomuchvariancewithineachsense.Figure5illustrateshowtheenergysenseofthewordpower(violet/6inFigure4)haschangedovertime.Characteristictermsforagivensensearehighlightedinboldface.Forexample,theterm“water”isinitiallyprevalent,whiletheterm“steam”risesinprevalencetowardsthemiddleofthemodeledperiod,andissupersededbytheterms“plant”and“nuclear”towardstheend.6Experiment2:NovelSenseDetectionInthissectionandthenextwewillexplicitlyeval-uatethetemporalrepresentations(i.e.,probabilitydistributions)inducedbyourmodel,anddiscussitsperformanceinthecontextofpreviouswork.Large-scaleevaluationofmeaningchangeisnoto-riouslydifﬁcult,andmanyevaluationsarebasedonlimitedhand-annotatedgoldstandarddatasets.Mi-traetal.(2015),然而,bypassthisissuebyeval-uatingtheoutputoftheirsystemagainstWordNet(Fellbaum,1998).这里,weconsidertheirauto-maticevaluationofsense-births,i.e.,theemergenceofnovelsenses.Weassumethatnovelsensesaredetectedatafocustimet2whilstbeingcomparedtoareferencetimet1.WordNetisusedtoconﬁrmthattheproposednovelsenseisindeeddistinctfromallotherinducedsensesforagivenword.MethodMitraetal.’s(2015)evaluationmethodpresupposesasystemwhichisabletodetectsensesforasetoftargetwordsandidentifywhichonesarenovel.Ourmodeldoesnotautomaticallyyieldnov-eltyscoresfortheinducedsenses.However,Cooketal.(2014)proposeseveralwaystoperformthistaskpost-hoc.Weusetheirrelevancescore,whichisbasedontheintuitionthatkeywords(orcollo-cations)whichcharacterizethedifferenceofafo-cuscorpusfromareferencecorpusareindicativeofwordsensenovelty.Weidentifykeywordsforafocuscorpuswithre-specttoareferencecorpususingKilgarriff’s(2009)methodwhichisbasedonsmoothedrelativefre-quencies.5Thenoveltyofaninducedsensescanbethendeﬁnedintermsoftheaggregatekeywordprobabilitiesgiventhatsense(andfocustimeofin-terest):相对(s)=Xw∈Wp(w|s,t2).(4)whereWisakeywordlistandt2thefocustime.Cooketal.(2014)suggestastraightforwardextrap-olationfromsensenoveltytowordnovelty:相对(C)=maxsrel(s),(5)5Wesetthesmoothingparameterton=10,andlikeCooketal.(2014)retrievethetop1000keywords. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 39 t1=1900–1919t2=1980–1999unionsovietunitedamericanunioneuropeanwarcivilmilitarypeoplelibertydossystemwindowdiskpcoperateprogramruncomputerdedosentertainmenttelevisionindustryprogramtimebusinesspeopleworldpresidententertainmentcompanystationradiostationtelevisionlocalprogramnetworkspacetvbroadcastairt1=1960–1969t2=1990–1999environmentalsupranotelawprotectionidagencyimpactpolicyfactorfederaluserscomputerwindowinformationsoftwaresystemwirelessdrivewebbuildingavailablevirtualrealityvirtualcomputercenterexperienceweekcommunityseparationincreasediskharddiskdriveprogramcomputerﬁlestorerambusinessemboldenTable2:Exampletargetterms(左边)withnovelsenses(正确的)asidentiﬁedbySCANinfocuscorpust2(whencomparedagainstreferencecorpust1).Top:termsusedinnovelsensedetectionstudy(Experiment2).Bottom:termsfromtheGulordavaandBaroni(2011)goldstandard(Experiment3).whererel(C)isthehighestnoveltyscoreassignedtoanyofthetargetword’ssenses.Ahighrel(C)scoresuggeststhatawordhasundergonemeaningchange.Weobtainedcandidatetermsandtheirassoci-atednovelsensesfromtheDATEcorpus,usingtherelevancemetricdescribedabove.Thenovelsensesfromthefocusperiodandallsensesinducedforthereferenceperiod,exceptfortheonecorre-spondingtothenovelsense,werepassedontoMitraetal.’s(2015)WordNet-basedevaluatorwhichpro-ceedsasfollows.Firstly,eachinducedsensesismappedtotheWordNetsynsetuwiththemaximumoverlap:synset(s)=argmaxuoverlap(s,你).(6)下一个,apredictednovelsensenisdeemedtrulynovelifitsmappedsynsetisdistinctfromanysynsetmappedtoadifferentinducedsense:∀s0synset(s0)6=synset(n).(7)最后,overallprecisioniscalculatedasthefrac-tionofsense-birthsconﬁrmedbyWordNetoverallbirth-candidatesproposedbythemodel.LikeMitraetal.(2015)weonlyreportresultsontargetwordsforwhichallinducedsensescouldbesuccessfullymappedtoasynset.ModelsandParametersWeobtainedthebroadsetoftargetwordsusedforthetask-basedevalua-tion(inSection8)andtrainedmodelsontheDATEcorpus.WesetthenumberofsensesK=4fol-lowingMitraetal.(2015)whonotethattheWord-Netmapperworksbestforwordswithasmallnum-berofsenses,andthetimeintervalsto∆T=20asinExperiment1.Weidentiﬁed200words6withhighestnoveltyscore(方程(5))assensebirthcandidates.WecomparedtheperformanceofthefullSCANmodelagainstSCAN-NOTwhichlearnssensesindependentlyfortimeintervals.Wetrainedbothmodelsonthesamedatawithidenticalpa-rameters.ForSCAN-NOT,wemustpost-hociden-tifycorrespondingsensesacrosstimeintervals.WeusedtheJensen-Shannondivergencebetweenthereference-andfocus-timespeciﬁcworddistribu-tionsJS(p(w|s,t1)||p(w|s,t2))andassignedeachfocus-timesensetothesensewithsmallestdiver-genceatreferencetime.ResultsFigure6showstheperformanceofourmodelsonthetaskofsensebirthdetection.SCANperformsbetterthanSCAN-NOT,underscoringtheimportanceofjointmodelingofsensesacrosstimeslicesandincorporationoftemporaldynamics.OuraccuracyscoresareinthesameballparkasMitraetal.(2014,2015).笔记,howeverthatthescoresarenotdirectlycomparableduetodifferencesintrain-ingcorpora,focusandreferencetimes,andcandi-datewords.Mitraetal.(2015)usethelargerGooglesyntacticn-gramcorpus,aswellasricherlinguis-ticinformationintermsofsyntacticdependencies.Weshowthatourmodelwhichdoesnotrelyonsyntacticannotationsperformscompetitivelyevenwhentrainedonsmallerdata.Table2(顶部)displaysexamplesofwordsassignedhighestnoveltyscoresforthereferenceperiod1900–1919andfocuspe-riod1980–1999.6Thisthresholdwastunedononereference-focustimepair. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 40 7Experiment3:WordMeaningChangeInthisexperimentweevaluatewhethermodelin-ducedtemporalwordrepresentationscaptureper-ceivedwordnovelty.Speciﬁcally,weadopttheeval-uationframework(anddataset)introducedinGulor-davaandBaroni(2011)7anddiscussedbelow.MethodGulordavaandBaroni(2011)donotmodelwordsensesdirectly;insteadtheyobtaindis-tributionalrepresentationsofwordsfromtheGoogleBooks(bigram)datafortwotimeslices,namelythe1960s(referencecorpus)and1990s(focuscor-pus).Todetectchangeinmeaning,theymeasurecosinesimilaritybetweenthevectorrepresentationsofatargetwordinthereferenceandfocuscorpus.Itisassumedthatlowsimilarityindicatessigniﬁ-cantmeaningchange.Toevaluatetheoutputoftheirsystem,theycreatedatestsetof100targetwords(nouns,动词,andadjectives),andaskedﬁveanno-tatorstorateeachwordwithrespecttoitsdegreeofmeaningchangebetweenthe1960sandthe1990s.Theannotatorsuseda4-pointordinalscale(0:nochange,1:almostnochange,2:somewhatchange,3:changedsigniﬁcantly).Wordsweresubsequentlyrankedaccordingtothemeanratinggivenbytheannotators.Inter-annotatoragreementonthenovelsensedetectiontaskwas0.51(pairwisePearsoncor-relation)andcanberegardedasanupperboundonmodelperformance.ModelsandParametersWetrainedmodelsforallwordsinGulordavaandBaroni’s(2011)gold-standard.WeusedtheDATEsubcorpuscover-ingyears1960through1999partitionedbydecade(∆T=10).Theﬁrstandlasttimeintervalweredeﬁnedasreferenceandfocustime,respec-tively(t1=1960–1969,t2=1990–1999).AsinEx-periment2,anoveltyscorewasassignedtoeachtargetword(usingEquation(5)).WecomputedSpearman’sρrankcorrelationsbetweengoldstan-dardandmodelrankings(GulordavaandBaroni,2011).WetrainedSCANmodelssettingthenum-berofsensestoK=8.WealsotrainedSCAN-NOTmodelswithidenticalparameters.Wereportresultsaveragedoverﬁveindependentparameterestimates.Finally,asinGulordavaandBaroni(2011)wecom-pareagainstafrequencybaselinewhichrankswords7WethankKristinaGulordavaforsharingtheirevaluationdatasetoftargetwordsandhumanjudgments.systemcorpusSpearman’sρGulordava(2011)Google0.386SCANDATE0.377SCAN-NOTDATE0.255frequencybaselineDATE0.325Table3:Spearman’sρrankcorrelationsbetweensystemnoveltyrankingsandthehuman-producedratings.Allcorrelationsarestatisticallysigniﬁcant(p<0.02).Re-sultsforSCANandSCAN-NOTareaveragesoverﬁvetrainedmodels.bytheirlogrelativefrequencyinthereferenceandfocuscorpus.ResultsTheresultsofthisevaluationareshowninTable3.Ascanbeseen,SCANoutperformsSCAN-NOTandthefrequencybaseline.Forrefer-ence,wealsoreportthecorrelationcoefﬁcientob-tainedinGulordavaandBaroni(2011)butempha-sizethatthescoresarenotdirectlycomparableduetodifferencesintrainingdata:GulordavaandBa-roni(2011)usetheGooglebigramscorpus(whichismuchlargercomparedtoDATE).Table2(bottom)displaysexamplesofwordswhichachievedhighestnoveltyscoresinthisevaluation,andtheirassociatednovelsenses.8Experiment4:Task-basedEvaluationIntheprevioussectionswedemonstratedhowSCANcapturesmeaningchangebetweentwoperiods.Inthissection,weassessourmodelonanextrinsictaskwhichreliesonmeaningrepresentationsspan-ningseveraltimeslices.WequantitativelyevaluateourmodelontheSemEval-2015benchmarkdatasetsreleasedaspartoftheDiachronicTextEvaluationexercise(PopescuandStrapparava2015;DTE).InthefollowingweﬁrstpresenttheDTEsubtasks,andthenmoveontodescribeourtrainingdata,parame-tersettings,andsystemsusedforcomparisontoourmodel.SemEvalDTETasksDiachronictextevaluationisanumbrellatermusedbytheSemEval-2015or-ganizerstorepresentthreesubtasksaimingtoassesstheperformanceofcomputationalmethodsusedtoidentifywhenapieceoftextwaswritten.Asimi-larproblemistackledinChambers(2012)whola-beldocumentswithtimestampswhilstfocusingon l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 41 explicittimeexpressionsandtheirdiscriminatorypower.TheSemEvaldataconsistsofnewssnip-pets,whichrangebetweenafewwordsandmul-tiplesentences.Asetoftrainingsnippets,aswellasgold-annotateddevelopmentandtestdatasetsareprovided.DTEsubtasks1and2involvetempo-ralclassiﬁcation:givenanewssnippetandasetofnon-overlappingtimeintervalscoveringthepe-riod1700through2010,thesystem’staskistose-lecttheintervalcorrespondingtothesnippet’syearoforigin.Temporalintervalsareconsecutiveandconstructedsuchthatthecorrectintervaliscenteredaroundtheactualyearoforigin.Forbothtaskstem-poralintervalsarecreatedatthreelevelsofgranular-ity(ﬁne,medium,andcoarse).Subtask1involvessnippetswhichcontainanex-plicitcuefortimeoforigin.Thepresenceofatemporalcuewasdeterminedbytheorganizersbycheckingtheentities’informativenessinexternalre-sources.Considertheexamplebelow:(8)PresidentdeGaullefavorsanindependentEuropeannuclearstrikingforce[...]ThementionsofFrenchpresidentdeGaulleandnu-clearwarfaresuggestthatthesnippetwaswrittenafterthemid-1950sandindeeditwaspublishedin1962.Ahypotheticalsystemwouldthenhavetode-cideamongstthefollowingclasses:{1700–1702,1703–1705,...,1961–1963,...,2012–2014}{1699–1706,1707–1713,...,1959–1965,...,2008–2014}{1696–1708,1709–1721,...,1956–1968,...,2008–2020}Theﬁrstsetofclassescorrespondtoﬁne-grainedin-tervalsof2-years,thesecondsettomedium-grainedintervalsof6-yearsandthethirdsettocoarse-grainedintervalsof12-years.Forthesnippetinexample(8)classes1961–1963,1959–1965,and1956–1968arethecorrectones.Subtask2involvestemporalclassiﬁcationofsnip-petswhichlackexplicittemporalcues,butcontainimplicitones,e.g.,asindicatedbylexicalchoiceorspelling.Thesnippetinexample(9)waspublishedin1891andthespellingofto-day,whichwascom-monuptotheearly20thcentury,isanimplicitcue:(9)Thelocalwheatmarketwasnotquitesostrongto-dayasyesterday.Analogouslytosubtask1,systemsmustselecttherighttemporalintervalfromasetofcontiguoustimeintervalsofdifferinggranularity.Forthistask,whichisadmittedlyharder,levelsoftemporalgran-ularityarecoarsercorrespondingto6-year,12-yearand20-yearintervals.ParticipatingSemEvalSystemsWecomparedourmodelagainstthreeothersystemswhichpar-ticipatedintheSemEvaltask.8AMBRA(Zampierietal.,2015)adoptsalearning-to-rankmodelingap-proachandusesseveralstylistic,grammatical,andlexicalfeatures.IXA(Salaberrietal.,2015)usesacombinationofapproachestodeterminethepe-riodoftimeinwhichapieceofnewswaswrit-ten.Thisinvolvessearchingforspeciﬁcmentionsoftimewithinthetext,searchingfornamedenti-tiespresentinthetextandthenestablishingtheirreferencetimebylinkingthesetoWikipedia,usingGooglen-grams,andlinguisticfeaturesindicativeoflanguagechange.Finally,UCD(SzymanskiandLynch,2015)employsSVMsforclassiﬁcationus-ingavarietyofinformativefeatures(e.g.,POS-tagn-grams,syntacticphrases),whichwereoptimizedforthetaskthroughautomaticfeatureselection.ModelsandParametersWetrainedourmodelforindividualwordsandobtainedrepresentationsoftheirmeaningfordifferentpointsintime.Oursetoftargetwordsconsistedofallnounswhichoc-curredinthedevelopmentdatasetsforDTEsub-tasks1and2aswellasallverbswhichoccurredatleasttwiceinthisdataset.Afterremovingin-frequentwordswewereleftwith883words(outof1,116)whichweusedinthisevaluation.Targetwordswerenotoptimizedwithrespecttothetestdatainanyway;itisthusreasonabletoexpectbet-terperformancewithanadjustedsetofwords.Wesetthemodeltimeintervalto∆T=5yearsandthenumberofsensesperwordtoK=8.WealsoevaluatedSCAN-NOT,thestripped-downversionofSCAN,withidenticalparameters.BothSCANandSCAN-NOTpredictthetimeoforiginforatestsnippetasfollows.Weﬁrstdetectmentionsoftargetwordsinthesnippet.Then,foreachmen-tioncweconstructadocument,akintothetrainingdocuments,consistingofcanditscontextw,the±5wordssurroundingc.Given{c,w},weapproximate8WedonotreportresultsforthesystemUSAARwhichachievedcloseto100%accuracybysearchingforthetestsnip-petsontheweb,withoutperforminganytemporalinference. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 42 Task1Task22yr6yr12yr6yr12yr20yraccpaccpaccpaccpaccpaccpBaseline.097.010.214.017.383.046.199.025.343.047.499.057SCAN-NOT.265.086.435.139.609.169.259.041.403.056.567.098SCAN.353.049.569.112.748.206.376.053.572.091.719.135IXA.187.020.375.041.557.090.261.037.428.067.622.098AMBRA.167.037.367.071.554.074.605.143.767.143.868.292UCD––––––.759.463.846.472.910.542SVMSCAN.192.034.417.097.545.127.573.331.667.368.790.428SVMSCAN+ngram.222.030.467.079.627.142.747.481.821.500.897.569Table4:ResultsonDiachronicTextEvaluationTasks1and2forarandombaseline,ourSCANmodel,itsstripped-downversionwithoutiGMRFs(SCAN-NOT),theSemEvalsubmissions(IXA,AMBRAandUCD),andSVMstrainedwithSCANfeatures(SVMSCAN),andwithadditionalcharactern-gramfeatures(SVMSCAN+ngram).Resultsareshownforthreelevelsofgranularity,astrictprecisionmeasurep,andadistance-discountingmeasureacc.adistributionovertimeintervalsas:p(c)(t|w)∝p(c)(w|t)×p(c)(t)(10)wherethesuperscript(c)indicatesparametersfromtheword-speciﬁcmodel,wemarginalizeoversensesandassumeauniformdistributionovertimeslicesp(c)(t).Finally,wecombinetheword-wisepredic-tionsintoaﬁnaldistributionp(t)=Qcp(c)(t|,w),andpredictthetimetwithhighestprobability.SupervisedClassiﬁcationWealsoapplyourmodelinasupervisedsetting,i.e.,byextractingfeaturesforclassiﬁerprediction.Speciﬁcally,wetrainedamulticlassSVM(ChangandLin,2011)onthetrainingdataprovidedbytheSemEvalorganiz-ers(forDTEtasks1and2).Foreachobservedwordwithineachsnippet,weaddedasfeatureitsmostlikelysensekgivent,thetruetimeoforigin:argmaxkp(c)(k|t).(11)WealsotrainedamulticlassSVMwhichuseschar-actern-gram(n∈{1,2,3})featuresinadditiontothemodelfeatures.SzymanskiandLynch(2015)identiﬁedcharactern-gramsasthemostpredictivefeaturefortemporaltextclassiﬁcationusingSVMs.Theirsystem(UCD)achievedthebestpublishedscoresinDTEsubtask2.Followingtheirapproach,weincludedalln-gramsthatwereobservedmorethan20timesintheDTEtrainingdata.ResultsWeemployedtwoevaluationmeasuresproposedbytheDTEorganizers.Thesearepre-cisionp,i.e.,thepercentageoftimesasystemhaspredictedthecorrecttimeperiod.Andaccuracyaccwhichismorelenient,andpenalizessystempredic-tionsproportionaltotheirdistancefromthetruein-terval.WecomputethepandaccscoresforourmodelsusingtheevaluationscriptprovidedbytheSemEvalorganizers.Table4summarizesourre-sultsforDTEsubtasks1and2.WecompareSCANagainstabaselinewhichselectsatimeintervalatrandom9averagedoverﬁveruns.Wealsoshowre-sultsforastripped-downversionofourmodelwith-outtheiGMRFs(SCAN-NOT)andforthesystemswhichparticipatedinSemEval.Forsubtask1,thetwoversionsofSCANout-performallSemEvalsystemsacrosstheboard.SCAN-NOToccasionallyoutperformsSCANinthestrictprecisionmetric,however,thefullSCANmodelconsistentlyachievesbetteraccuracyscoreswhicharemorerepresentativesincetheyfactorintheproximityofthepredictiontothetruevalue.Insubtask2,theUCDandSVMSCAN+ngramsystemsperformcomparably.TheybothuseSVMsfortheclassiﬁcationtask,howeverourownmodelemploysalessexpressivefeaturesetbasedonSCANandcharactern-grams,anddoesnottakeadvantageoffeatureselectionwhichwouldpresumablyenhanceperformance.WiththeexceptionofAMBRA,allotherparticipatingsystemsusedexternalresources(suchasWikipediaandGooglen-grams);itisthusfairtoassumetheyhadaccesstoatleastasmuchtrainingdataasourSCANmodel.Consequently,the9Werecomputedthebaselinescoresforsubtasks1and2duetoinconsistenciesintheresultsprovidedbytheDTEorganizers. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - p d f / d o i / . 1 0 1 1 6 2 / t l a c _ a _ 0 0 0 8 1 1 5 6 7 3 7 0 / / t l a c _ a _ 0 0 0 8 1 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 43 gapinperformancecannotsolelybeattributedtoadifferenceinthesizeofthetrainingdata.WealsoobservethatIXAandSCAN,giveniden-ticalgranularity,performbetteronsubtask1,whileAMBRAandourownSVM-basedsystemsexhibittheoppositetrend.TheIXAsystemusesacombi-nationofknowledgesourcesinordertodeterminewhenapieceofnewswaswritten,includingex-plicitmentionsoftemporalexpressionswithinthetext,namedentities,andlinkedinformationtothosenamedentitiesfromWikipedia.AMBRAontheotherhandexploitsmoreshallowstylistic,grammat-icalandlexicalfeatureswithinthelearning-to-rankparadigm.Aninterestingdirectionforfutureworkwouldbetoinvestigatewhichfeaturesaremostap-propriatefordifferentDTEtasks.Overall,itisen-couragingtoseethatthegenerictemporalwordrep-resentationsinferredbySCANleadtocompetitivelyperformingmodelsonbothtemporalclassiﬁcationtaskswithoutanyexplicittuning.9ConclusionInthispaperweintroducedSCAN,adynamicBayesianmodelofdiachronicmeaningchange.Ourmodellearnsacoherentsetofco-dependenttime-speciﬁcsensesforindividualwordsandtheirprevalence.Evaluationofthemodel’soutputshowedthatthelearntrepresentationsreﬂect(a)dif-ferentsensesofambiguouswords(b)differentkindsofmeaningchange(suchasnewsensesbeingestab-lished),and(c)connotationalchangeswithinsenses.SCANdepartsfrompreviousworkinthatitmodelstemporaldynamicsexplicitly.Wedemonstratedthatthisfeatureyieldsmoregeneralsemanticrepresen-tationsasindicatedbypredictiveloglikelihoodandavarietyofextrinsicevaluations.Wealsoexperi-mentallyevaluatedSCANonnovelsensedetectionandtheSemEvalDTEtask,whereitperformedonparwiththebestpublishedresults,withoutanyex-tensivefeatureengineeringortaskspeciﬁctuning.Weconcludebydiscussinglimitationsofourmodelanddirectionsforfuturework.Inourexper-imentsweﬁxthenumberofsensesKforallwordsacrossalltimeperiods.Althoughthisapproachdidnotharmperformance(evenincaseofSemEvalwherewehandledmorethan800targetconcepts),itisatoddswiththefactthatwordsvaryintheirdegreeofambiguity,andthatwordsensescontinu-ouslyappearanddisappear.Anon-parametricver-sionofourmodelwouldinferanappropriatenumberofsensesfromthedata,individuallyforeachtimeperiod.Alsonotethatinourexperimentsweusedcontextasabagofwords.Itwouldbeinterestingtoexploremoresystematicallyhowdifferentkindsofcontexts(e.g.,namedentities,multiwordexpres-sions,verbsvs.nouns)inﬂuencetherepresentationsthemodellearns.Furthermore,whileSCANcap-turesthetemporaldynamicsofwordsenses,itcan-notdosoforwordsthemselves.Putdifferently,themodelcannotidentifywhetheranewwordisusedwhichdidnotexistbefore,orthatawordceasedtoexistafteraspeciﬁcpointintime.Amodelinternalwayofdetectingword(dis)appearancewouldbede-sirable,especiallysincenewtermsarecontinuouslybeingintroducedthankstopopularcultureandvari-ousnewmediasources.Inthefuture,wewouldliketoapplyourmodeltodifferenttextgenresandlevelsoftemporalgranular-ity.Forexample,wecouldworkwithTwitterdata,anincreasinglypopularsourceforopiniontracking,anduseourmodeltoidentifyshort-termchangesinwordmeaningsorconnotations.AcknowledgmentsWearegratefultotheanonymousreviewerswhosefeedbackhelpedtosubstantiallyimprovethepresentpaper.WethankCharlesSuttonandIainMurrayforhelpfuldiscussions,andacknowledgethesupportofEPSRCthroughprojectgrantEP/I037415/1.ReferencesAitchison,Jean.2001.LanguageChange:ProgressOrDecay?.CambridgeApproachestoLinguis-tics.CambridgeUniversityPress.Biemann,Chris.2006.ChineseWhispers-anEfﬁ-cientGraphClusteringAlgorithmanditsAppli-cationtoNaturalLanguageProcessingProblems.InProceedingsofTextGraphs:the1stWorkshoponGraphBasedMethodsforNaturalLanguageProcessing.NewYorkCity,NY,USA,pages73–80.Bird,Steven,EwanKlein,andEdwardLoper.2009.NaturalLanguageProcessingwithPython.O’ReillyMedia,Inc.,1stedition.Blei,DavidM.andJohnD.Lafferty.2006a.Cor- l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / t a c l / l a r t i c e - 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