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word2vec实践（一）：预备知识

2016-06-15 00:00 316 查看 
word2vec是google最新发布的深度学习工具，它利用神经网络将单词映射到低维连续实数空间，又称为单词嵌入。词与词之间的语义相似度可以通过两个单词的嵌入向量之间的余弦夹角直接衡量,更不用说使用诸如kmeans、层次聚类这样的算法来挖掘其功能了，同时作者TomasMikolov发现了比较有趣的现象，就是单词经过分布式表示后，向量之间依旧保持一定的语法规则，比如简单的加减法规则。

目前网络上有大量的实践文章和理论分析文章。主要列举如下：

理论分析文章：DeepLearning实战之word2vec

DeepLearninginNLP（一）词向量和语言模型
word2vec傻瓜剖析word2vec学习+使用介绍

实践部分：

利用中文数据跑Google开源项目word2vec分词工具ANSJ(实例)Word2vec在事件挖掘中的调研参考文献：



[1]TomasMikolov,KaiChen,GregCorrado,andJeffreyDean.EfficientEstimationofWordRepresentationsinVectorSpace.InProceedingsofWorkshopatICLR,2013.[2]TomasMikolov,IlyaSutskever,KaiChen,GregCorrado,andJeffreyDean.DistributedRepresentationsofWordsandPhrasesandtheirCompositionality.InProceedingsofNIPS,2013.[3]TomasMikolov,Wen-tauYih,andGeoffreyZweig.LinguisticRegularitiesinContinuousSpaceWordRepresentations.InProceedingsofNAACLHLT,2013.[4]TomasMikolov,StefanKombrink,LukasBurget,JanCernocky,andSanjeevKhudanpur.Extensionsofrecurrentneuralnetworklanguagemodel.InAcoustics,SpeechandSignalProcessing(ICASSP),2011,IEEEInternationalConferenceon,pages5528–5531.IEEE,2011.[5]TomasMikolov,KaiChen,
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GregCorrado,andJeffreyDean.Efficientestimationofwordrepresentationsinvectorspace.ICLRWorkshop,2013.[6]FredericMorinandYoshuaBengio.Hierarchicalprobabilisticneuralnetworklanguagemodel.InProceedingsoftheinternationalworkshoponartificialintelligenceandstatistics,pages246–252,2005.[7]AndriyMnihandGeoffreyEHinton.Ascalablehierarchicaldistributedlanguagemodel.Advancesinneuralinformationprocessingsystems,21:1081–1088,2009.[8]Hinton,GeoffreyE."Learningdistributedrepresentationsofconcepts."Proceedingsoftheeighthannualconferenceofthecognitivesciencesociety.1986.[9]R.Rosenfeld,"Twodecadesofstatisticallanguagemodeling:wheredowegofromhere?",ProceedingsoftheIEEE,88(8),1270-1288,2000.[10]JeffreyDean,GregS.Corrado,RajatMonga,KaiChen,MatthieuDevin,QuocV.Le,MarkZ.Mao,Marc’AurelioRanzato,AndrewSenior,PaulTucker,KeYang,andAndrewY.Ng."LargeScaleDistributedDeepNetworks".ProceedingsofNIPS,2012.

[11]http://licstar.net/archives/328
[12]http://www.cs.columbia.edu/~mcollins/loglinear.pdf
[13]A.MnihandG.Hinton.Threenewgraphicalmodelsforstatisticallanguagemodelling.Proceedingsofthe24thinternationalconferenceonMachinelearning,pages641–648,2007[14]FredericMorinandYoshuaBengio.Hierarchicalprobabilisticneuralnetworklanguagemodel.InRobertG.CowellandZoubinGhahramani,editors,AISTATS’05,

                                            

                                        

                        
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