Convolutional Neural Networks for Sentence Classification

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1、ConvolutionalNeuralNetworksforSentenceClassificationYoonKimNewYorkUniversityyhk255@nyu.eduAbstractlocalfeatures(LeCunetal.,1998).Originallyinventedforcomputervision,CNNmodelshaveWereportonaseriesofexperimentswithsubsequentlybeenshowntobeeffectiveforNLPconvolutionalneuralnetwo

2、rks(CNN)andhaveachievedexcellentresultsinsemantictrainedontopofpre-trainedwordvec-parsing(Yihetal.,2014),searchqueryretrievaltorsforsentence-levelclassificationtasks.(Shenetal.,2014),sentencemodeling(Kalch-WeshowthatasimpleCNNwithlit-brenneretal.,2014),andothertraditionalNLPt

3、lehyperparametertuningandstaticvec-tasks(Collobertetal.,2011).torsachievesexcellentresultsonmulti-Inthepresentwork,wetrainasimpleCNNwithplebenchmarks.Learningtask-specificonelayerofconvolutionontopofwordvectorsvectorsthroughfine-tuningoffersfurtherobtainedfromanunsupervisedneu

4、rallanguagegainsinperformance.Weadditionallymodel.ThesevectorsweretrainedbyMikolovetproposeasimplemodificationtothear-al.(2013)on100billionwordsofGoogleNews,chitecturetoallowfortheuseofbothandarepubliclyavailable.1Weinitiallykeepthetask-specificandstaticvectors.TheCNNwordvecto

5、rsstaticandlearnonlytheotherparam-modelsdiscussedhereinimproveupontheetersofthemodel.Despitelittletuningofhyper-stateofthearton4outof7tasks,whichparameters,thissimplemodelachievesexcellentincludesentimentanalysisandquestionresultsonmultiplebenchmarks,suggestingthatclassificat

6、ion.thepre-trainedvectorsare‘universal’featureex-1Introductiontractorsthatcanbeutilizedforvariousclassifica-tiontasks.Learningtask-specificvectorsthroughDeeplearningmodelshaveachievedremarkablefine-tuningresultsinfurtherimprovements.Weresultsincomputervision(Krizhevskyetal.,fina

7、llydescribeasimplemodificationtothearchi-2012)andspeechrecognition(Gravesetal.,2013)tecturetoallowfortheuseofbothpre-trainedandinrecentyears.Withinnaturallanguageprocess-task-specificvectorsbyhavingmultiplechannels.ing,muchoftheworkwithdeeplearningmeth-odshasinvolvedlearningwo

8、rdvectorrepresenta-OurworkisphilosophicallysimilartoRazaviantionsthroughneu