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1、SpeculationandNegationScopeDetectionviaConvolutionalNeuralNetworks11112ZhongQian,PeifengLi,QiaomingZhu,GuodongZhou,ZhunchenLuoandWei2Luo1SchoolofComputerScienceandTechnology,SoochowUniversity,Suzhou,215006,China2ChinaDefenseScienceandTechnologyInformationCenter,Bei
2、jing,100142,Chinaqianzhongqz@163.com,{pfli,qmzhu,gdzhou}@suda.edu.cn,zhunchenluo@gmail.com,htqxjj@126.comsentencesforexamples:Abstract(S1)Thedoctorswarnthatsmoking[mayharmourlungs].Speculationandnegationareimportantin-(S2)Hedoes[notlikeplayingfootball]butformationt
3、oidentifytextfactuality.Inthis1paper,weproposeaConvolutionalNeurallikesswimming.Network(CNN)-basedmodelwithprobabil-InsentenceS1,thespeculativecue“may”go-isticweightedaveragepoolingtoaddressvernsthescope“mayharmourlungs”,whilethespeculationandnegationscopedetection
4、.Innegativecue“not”governsthescope“notlikeparticular,ourCNN-basedmodelextractsplayingfootball”insentenceS2.thosemeaningfulfeaturesfromvarioussyn-tacticpathsbetweenthecuesandthecandi-Previousworkhaveachievedquitesuccessondatetokensinbothconstituencyanddepen-cueident
5、ification(e.g.,withF1-scoreof86.79dencyparsetrees.EvaluationonBioScopeforspeculativecuedetectioninTangetal.showsthatourCNN-basedmodelsignificant-(2010)).Incomparison,speculationandnegationlyoutperformsthestate-of-the-artsystemsonscopedetectionisstillachallengedueto
6、itsin-Abstracts,asub-corpusinBioScope,andherentdifficultiesandthoseupstreamerrors.InachievescomparableperformancesonClini-thispaper,wefocusonscopedetection.PreviouscalRecords,anothersub-corpusinBioScope.workonscopedetectioncanbeclassifiedintoheuristicrulesbasedmeth
7、ods(e.g.,Özgüretal.,1Introduction2009;Øvrelidetal.,2010),machinelearningFactualinformationiscriticaltounderstandabasedmethods(e.g.,Tangetal.,2010;Zouetal.,sentenceoradocumentinmosttypicalNLPap-2013),andhybridapproacheswhichintegrateplications.Speculationandnegation
8、extractionempiricalmodelswithmanualrules(Velldalethasbeendrawingmoreandmoreattentionsinal.,2012).recentyearsduetoitsimportanceindistinguish-Diffe
