Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks for Sentence Classification

ID：40358188

大?。?76.34 KB

頁數：6頁

時間：2019-07-31

Convolutional Neural Networks for Sentence Classification _第1頁
Convolutional Neural Networks for Sentence Classification _第2頁
Convolutional Neural Networks for Sentence Classification _第3頁
Convolutional Neural Networks for Sentence Classification _第4頁
Convolutional Neural Networks for Sentence Classification _第5頁
資源描述：

《Convolutional Neural Networks for Sentence Classification 》由會員上傳分享，免費在線閱讀，更多相關內容在學術論文-天天文庫。

1、ConvolutionalNeuralNetworksforSentenceClassi?cationYoonKimNewYorkUniversityyhk255@nyu.eduAbstractlocalfeatures(LeCunetal.,1998).Originallyinventedforcomputervision,CNNmodelshaveWereportonaseriesofexperimentswithsubsequentlybeenshowntobeeffectiveforNLPconvolutionalneuralnetwo

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

3、lehyperparametertuningandstaticvec-tasks(Collobertetal.,2011).torsachievesexcellentresultsonmulti-Inthepresentwork,wetrainasimpleCNNwithplebenchmarks.Learningtask-speci?conelayerofconvolutionontopofwordvectorsvectorsthrough?ne-tuningoffersfurtherobtainedfromanunsupervisedneu

4、rallanguagegainsinperformance.Weadditionallymodel.ThesevectorsweretrainedbyMikolovetproposeasimplemodi?cationtothear-al.(2013)on100billionwordsofGoogleNews,chitecturetoallowfortheuseofbothandarepubliclyavailable.1Weinitiallykeepthetask-speci?candstaticvectors.TheCNNwordvecto

5、rsstaticandlearnonlytheotherparam-modelsdiscussedhereinimproveupontheetersofthemodel.Despitelittletuningofhyper-stateofthearton4outof7tasks,whichparameters,thissimplemodelachievesexcellentincludesentimentanalysisandquestionresultsonmultiplebenchmarks,suggestingthatclassi?cat

6、ion.thepre-trainedvectorsare‘universal’featureex-1Introductiontractorsthatcanbeutilizedforvariousclassi?ca-tiontasks.Learningtask-speci?cvectorsthroughDeeplearningmodelshaveachievedremarkable?ne-tuningresultsinfurtherimprovements.Weresultsincomputervision(Krizhevskyetal.,?na

7、llydescribeasimplemodi?cationtothearchi-2012)andspeechrecognition(Gravesetal.,2013)tecturetoallowfortheuseofbothpre-trainedandinrecentyears.Withinnaturallanguageprocess-task-speci?cvectorsbyhavingmultiplechannels.ing,muchoftheworkwithdeeplearningmeth-odshasinvolvedlearningwo

8、rdvectorrepresenta-OurworkisphilosophicallysimilartoRazaviantionsthroughneu

當前文檔最多預覽五頁，下載文檔查看全文

此文檔下載收益歸作者所有

當前文檔最多預覽五頁，下載文檔查看全文
溫馨提示：
1. 部分包含數學公式或PPT動畫的文件，查看預覽時可能會顯示錯亂或異常，文件下載后無此問題，請放心下載。
2. 本文檔由用戶上傳，版權歸屬用戶，天天文庫負責整理代發(fā)布。如果您對本文檔版權有爭議請及時聯系客服。
3. 下載前請仔細閱讀文檔內容，確認文檔內容符合您的需求后進行下載，若出現內容與標題不符可向本站投訴處理。
4. 下載文檔時可能由于網絡波動等原因無法下載或下載錯誤，付費完成后未能成功下載的用戶請聯系客服處理。