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1、EvaluationofSyntacticPhraseIndexing
2、CLARITNLPTrackReport1122ChengxiangZhai,XiangTong,NatasaMilic-Frayling,DavidA.Evans1LaboratoryforComputationalLinguisticsCarnegieMellonUniversityPittsburgh,PA15213fcz25@andrew.cmu.edu,xt22@andrew.cmu.edug2CLARITECHCorpora
3、tion5301FifthAve.Pittsburgh,PA15232-2124fnatasa@clarit.com,dae@clarit.comg1IntroductionTM1TheCLARITNLPtracke ortisfocusedonevaluatingtheusefulnessofsyntacticphrasesfordocumentindexing.TheCLARITsystemhasseveralNLPtechniquesintegratedwiththevectorspaceretrieva
4、lmodel[Evansetal.91,Evansetal.95].TheNLPtechniquesusedinCLARITincludemorphologicalanalysis,robustnoun-phraseparsing,andautomaticconstructionof rstorderthesauri,amongothers.OnemainfeatureofCLARITindexingisthatitemphasizesphrase-basedindexingwithdi erentoption
5、sfordecomposingnounphrasesintosmallerconstituents,includingsinglewords.InpastTRECs,thedefaultmodeforindexinginvolvedfullnounphrases,singlewords,2andoccasionallyselectedsub-phrases.Whilesomeearlyexperimentshaveshownthee ectivenessofnounphrasesforindexing[Evan
6、setal.91],thereisnodirectevaluationoftheire ectivenessinthecontextofTREC.Inparticular,thecontributionofsmallsub-phrasestoretrievalperformancehasnotbeenevaluatedoutsidethecontextofoverallsystemperformance.TheversionoftheCLARITsystemthatweusedintheexperimentsh
7、asitsNLPcomponenttightlyintegratedwiththerestofthesystem.ThisdoesnotalloweasyevaluationoftheindividualNLPcomponents.We,therefore,developedseparateNLPmodulesandusedCLARITasaretrievalengineonlytoevaluatethee ectivenessofphrase-basedindexing.2Phrasesforindexing
8、MostcurrentIRsystemsuseword-basedindexingoftensupplementedwithphrasesthatareobtainedusingsimplestatisticalapproaches,suchasafrequencycountingofadjacentwordpairs.Intuitively,itseemsthatsyntacticphrases(i.e.,phraseswithcertainsyntacticrelations)canrepresentdoc
9、umentcontentmorepreciselythansinglewordsandphrasesdiscoveredusingsimplestatisticalmethods,andarethuspotentiallyusefulforindexing[Evansetal.91,Evansetal.95,Strzalkowskietal.95,Evans&Zhai96,Hullet
