简介:Sentimentanalysis,ahotresearchtopic,presentsnewchallengesforunderstandingusers'opinionsandjudg-mentsexpressedonline.Theyaimtoclassifythesubjectivetextsbyassigningthemapolaritylabel.Inthispaper,weintroduceanovelmachinelearningframeworkusingauto-encodersnetworktopredictthesentimentpolaritylabelatthewordlevelandthesentencelevel.Inspiredbythedimensionalityreductionandthefeatureextractioncapabilitiesoftheauto-encoders,weproposeanewmodelfordistributedwordvectorrepresentation"PMI-SA"usingasinputpointwise-mutual-information"PMI"wordvectors.Theresultedcontinuouswordvectorsarecombinedtorepresentasentence.Anunsupervisedsentenceembeddingmethod,calledContextualRecursiveAuto-Encoders"CoRAE",isalsodevelopedforlearningsentencerepresentation.Indeed,CoRAEfollowsthebasicideaoftherecursiveauto-encoderstodeeplycomposethevectorsofwordsconstitutingthesentence,butwithoutrelyingonanysyntacticparsetree.TheCoRAEmodelconsistsincombiningrecursivelyeachwordwithitscontextwords(neighbors'words:previousandnext)byconsideringthewordorder.Asupportvectormachineclassifierwithfine-tuningtechniqueisalsousedtoshowthatourdeepcompositionalrepresentationmodelCoRAEimprovessignificantlytheaccuracyofsentimentanalysistask.Experimentalresultsdemon-stratethatCoRAEremarkablyoutperformsseveralcompetitivebaselinemethodsontwodatabases,namely,SanderstwittercorpusandFacebookcommentscorpus.TheCoRAEmodelachievesanefficiencyof83.28%withtheFacebookdatasetand97.57%withtheSandersdataset.
简介:本文针对轴类零件的设计和管理问题,以Pro/ENGINEER软件的二次发为例,通过研究PDM平台管理数据的方式和Pro/ENGINEER参数化建模的方法,介绍了利用Pro/toolkit插件在Pro/ENGINEER软件平台上发一种基于特征描述的轴类零件CAD系统。