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1、ExtendingABroad-CoverageParserforaGeneralNLPToolkitHassanAlam,HuaCheng,RachmatHartono,AmanKumar,PaulLlido,CrystalNakatsu,FuadRahman,YuliyaTarnikova,TimotiusTjahjadiandCheWilcoxBCLTechnologiesInc.SantaClara,CA95050U.S.A.fuad@bcltechnologies.comsimilarityandgeneratealargesetofsynt
2、acticAbstractvariationsofagivensentence.ItwillalsoWiththerapidgrowthofrealworldgeneratethecodethattakesauser’snaturalapplicationsforNLPsystems,therelanguagerequestandexecutesacommandonisagenuinedemandforageneralanapplication.Currentlythisisanactivetoolkitfromwhichprogrammersrese
3、archarea,andtheAdvancedTechnologywithnolinguisticknowledgecanProgram(ATP)oftheNationalInstituteofbuildspecificNLPsystems.SuchaStandardsandTechnology(NIST)isfundingtoolkitshouldhaveaparserthatispartofthework.generalenoughtobeusedacrossInordertohandlenaturallanguageinput,domains,a
4、ndyetaccurateenoughforanNLPtoolkitmusthaveaparserthatmapsaeachspecificapplication.Inthissentencestringtoasyntacticstructure.Thepaper,wedescribeaparserthatparsermustbebothgeneralandaccurate.Itextendsabroad-coverageparser,hastobegeneralbecauseprogrammersfromMinipar(Lin,2001),witha
5、ndifferentdomainswillusethetoolkittoadaptableshallowparsersoastogeneratetheirspecificparsers.IthastobeachievebothgeneralityandaccuracyaccuratebecausethetoolkittargetscommercialinhandlingdomainspecificNLdomains,whichusuallyrequirehighaccuracy.problems.WetestthisparseronourTheaccu
6、racyoftheparserdirectlyaffectsthecorpusandtheresultsshowthattheaccuracyofthegeneratedNLinterface.Intheaccuracyissignificantlyhigherthanprogram-through-exampleapproach,theasystemthatusesMiniparalone.toolkitshouldconverttheexamplesentencesintosemanticrepresentationssoastocapture1I
7、ntroductiontheirmeanings.Inarealworldapplication,thisprocesswillinvolvealargequantityofdata.IfWiththeimprovementofnaturallanguagetheprogrammershavetocheckeachsyntacticprocessing(NLP)techniques,domainsfororsemanticformbyhandinordertodecideifNLPsystems,especiallythosehandlingspeec
8、hthecorrespondingsentenceisparsedcorrectly,inpu