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Lexicalized Stochastic Modeling of Constraint-Based Grammars ...

Lexicalized Stochastic Modeling of Constraint-Based Grammars ...

Lexicalized Stochastic Modeling of Constraint-Based Grammars

InProceedingsofthe38thAnnualMeetingortheACL,2000,HongKong. LexicalizedStochasticModelingofConstraint-BasedGrammars usingLog-LinearMeasuresandEMTraining StefanRiezler IMS,UniversitätStuttgart riezler@ims.uni-stuttgart.de DetlefPrescher IMS,UniversitätStuttgart prescher@ims.uni-stuttgart.de JonasKuhn IMS,UniversitätStuttgart jonas@ims.uni-stuttgart.de MarkJohnson Cog.&Ling.Sciences,BrownUniversity Mark_Johnson@brown.edu Abstract Wepresentanewapproachto stochasticmodelingofconstraint- basedgrammarsthatisbasedonlog- linearmodelsandusesEMforesti- mationfromunannotateddata.The techniquesareappliedtoanLFG grammarforGerman.Evaluationon anexactmatchtaskyields86%pre- cisionforanambiguityrateof5.4, and90%precisiononasubcatframe matchforanambiguityrateof25. Experimentalcomparisontotrain- ingfromaparsebankshowsa10% gainfromEMtraining.Also,anew class-basedgrammarlexicalizationis presented,showinga10%gainover unlexicalizedmodels. 1Introduction Stochasticparsingmodelscapturingcontex- tualconstraintsbeyondthedependenciesof probabilisticcontext-freegrammars(PCFGs) arecurrentlythesubjectofintensiveresearch. Aninterestingfeaturecommontomostsuch modelsistheincorporationofcontextualde- pendenciesonindividualheadwordsintorule- basedprobabilitymodels.Suchword-based lexicalizationsofprobabilitymodelsareused successfullyinthestatisticalparsingmod- elsof,e.g.,Collins(1997),Charniak(1997), orRatnaparkhi(1997).However,itisstill anopenquestionwhichkindoflexicaliza- tion,e.g.,statisticsonindividualwordsor statisticsbaseduponwordclasses,isthebest choice.Secondly,theseapproacheshavein commonthefactthattheprobabilitymodels aretrainedontreebanks,i.e.,corporaofman- uallydisambiguatedsentences,andnotfrom corporaofunannotatedsentences.Inallofthe citedapproaches,thePennWallStreetJour- nalTreebank(Marcusetal.,1993)isused, theavailabilityofwhichobviatesthestandard eortrequiredfortreebanktraininghand- annotatinglargecorporaofspecicdomains ofspeciclanguageswithspecicparsetypes. Moreover,commonwisdomisthattraining fromunannotateddataviatheexpectation- maximization(EM)algorithm(Dempsteret al.,1977)yieldspoorresultsunlessat leastpartialannotationisapplied.Experi- mentalresultsconrmingthiswisdomhave beenpresented,e.g.,byElworthy(1994)and PereiraandSchabes(1992)forEMtraining ofHiddenMarkovModelsandPCFGs. Inthispaper,wepresentanewlexicalized stochasticmodelforconstraint-basedgram- marsthatemploysacombinationofhead- wordfrequenciesandEM-basedclustering forgrammarlexicalization.Furthermore,we makecrucialuseofEMforestimatingthe parametersofthestochasticgrammarfrom unannotateddata.OurusageofEMwasini- tiatedbythecurrentlackoflargeunication- basedtreebanksforGerman.However,ourex- perimentalresultsalsoshowanexceptionto thecommonwisdomoftheinsuciencyofEM forhighlyaccuratestatisticalmodeling. Ourapproachtolexicalizedstochasticmod- elingisbasedontheparametricfamilyoflog- linearprobabilitymodels,whichisusedtode- neaprobabilitydistributionontheparses ofaLexical-FunctionalGrammar(LFG)for German.Inpreviousworkonlog-linearmod- elsforLFGbyJohnsonetal.(1999),pseudo-

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