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1、RecursiveNeuralConditionalRandomFieldsforAspect-basedSentimentAnalysisWenyaWang†‡SinnoJialinPan†DanielDahlmeier‡XiaokuiXiao††NanyangTechnologicalUniversity,Singapore‡SAPInnovationCenterSingapore†{wa0001ya,sinnopan,xkxiao}@ntu.edu.sg,‡{d.dahlmeier}@sap.comAbstractliverytimesinthec
2、ity.”,theaspecttermisdeliverytimes,andtheopiniontermisfastest.Inaspect-basedsentimentanalysis,extract-Amongpreviouswork,oneoftheapproachesingaspecttermsalongwiththeopinionsbe-istoaccumulateaspectandopiniontermsfromaingexpressedfromuser-generatedcontentisseedcollectionwithoutlabel
3、information,byutiliz-oneofthemostimportantsubtasks.Previ-ingsyntacticrulesormodificationrelationsbetweenousstudieshaveshownthatexploitingcon-nectionsbetweenaspectandopiniontermsisthem(Qiuetal.,2011;Liuetal.,2013b).Inthepromisingforthistask.Inthispaper,wepro-aboveexample,ifweknowfa
4、stestisanopinionposeanoveljointmodelthatintegratesrecur-word,thendeliverytimesisprobablyinferredtobesiveneuralnetworksandconditionalrandomanaspectbecausefastestisitsmodifier.However,fieldsintoaunifiedframeworkforexplicitas-thisapproachlargelyreliesonhand-codedrulesandpectandopiniont
5、ermsco-extraction.TheisrestrictedtocertainPart-of-Speech(POS)tags,proposedmodellearnshigh-leveldiscrimina-e.g.,opinionwordsarerestrictedtobeadjectives.tivefeaturesanddoublepropagatesinforma-tionbetweenaspectandopinionterms,simul-Anotherapproachfocusesonfeatureengineeringtaneously
6、.Moreover,itisflexibletoincor-basedonpredefinedlexicons,syntacticanalysis,poratehand-craftedfeaturesintotheproposedetc.(JinandHo,2009;Lietal.,2010).Asequencemodeltofurtherboostitsinformationextrac-labelingclassifieristhenbuilttoextractaspectandtionperformance.Experimentalresultsonth
7、eopinionterms.Thisapproachrequiresextensiveef-datasetfromSemEvalChallenge2014task4fortsfordesigninghand-craftedfeaturesandonlyshowthesuperiorityofourproposedmodelcombinesfeatureslinearlyforclassificationwhichoverseveralbaselinemethodsaswellastheignoreshigherorderinteractions.winni
8、ngsystemsofthechallenge.Toovercomethelim