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1、AmbiguityResolutionforVt-NStructuresinChinese1,221Yu-MingHsiehJasonS.ChangKeh-JiannChen1InstituteofInformationScience,AcademiaSinica,Taiwan2DepartmentofComputerScience,NationalTsing-HuaUniversity,Taiwanmorris@iis.sinica.edu.tw,jason.jschang@gmail.comkchen@iis.sinica.edu.twmallyaVPstructure.Ho
2、wever,ChineseverbsAbstractcanalsomodifynounswithoutmorphologicalinflection,e.g.,養殖/farming池/pond.Conse-Thesyntacticambiguityofatransitivequently,parsingVt-Nstructuresisdifficultbe-verb(Vt)followedbyanoun(N)hascauseitishardtoresolvesuchambiguitieswith-longbeenaprobleminChineseparsing.outpriork
3、nowledge.ThefollowingaresomeInthispaper,weproposeaclassifiertotypicalexamplesofvariousVt-Nstructures:resolvetheambiguityofVt-Nstructures.Thedesignoftheclassifierisbasedon1)threeimportantguidelines,namely,解決/solve問題/problemVPadoptinglinguisticallymotivatedfeatures,解決/solving方案/methodNPusinga
4、llavailableresources,andeasyin-解決/solve人類/mankind(問題/problem)Nonetegrationintoaparsingmodel.Thelin-Tofindthemosteffectivedisambiguationfea-guisticallymotivatedfeaturesincludetures,weneedmoreinformationabouttheVt-Nsemanticrelations,context,andmorpho-NPconstructionandthesemanticrelationslogic
5、alstructures;andtheavailablere-betweenVtandN.StatisticaldatafromtheSini-sourcesaretreebank,thesaurus,affixda-caTreebank(Chenetal.,2003)indicatesthattabase,andlargecorpora.Wealsopro-58%ofVt-Nstructuresareverbphrases,16%posetwolearningapproachesthatresolvearenounphrases,and26%donothaveanyde-the
6、problemofdatasparsenessbyauto-pendentrelations.ItisobviousthatthesemanticparsingandextractingrelativerelationsbetweenaVt-Nstructureanditscon-knowledgefromlarge-scaleunlabeledtextinformationareveryimportantfordifferen-data.Ourexperimentresultsshowthattiatingbetweendependentrelations.Althoughth
7、eVt-Nclassifieroutperformsthecur-theverb-argumentrelationofVPstructuresisrentPCFGparser.Furthermore,itcanbewellunderstood,itisnotclearwhatkindofse-easilyandeffectivelyintegratedintothemanticrelationsresultinNPstructures.InthePCFGparserandgene