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。