参考文献

[1]Ziemski M，Junczys-Dowmunt M，Pouliquen B.The United Nations Parallel Corpus v1.0 [C]//Proceedings of LREC.2016.

[2]Koehn P，Hoang H，Birch A，et al.Moses：Open source toolkit for statistical machine translation[C]//Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions.Association for Computational Linguistics，2007：177-180.

[3]Che W，Li Z，Liu T.LTP：A Chinese language technology platform[C]//Proceedings of the 23rd International Conference on Computational Linguistics：Demonstrations.Association for Computational Linguistics，2010：13-16.

[4]Weaver W.Translation，Milestones in Machine Translation[M]//Locke W N，Booth A D.（eds）.Machine Translation of Languages：fourteen essays.Cambridge，Massachusetts：MIT Press，1955：15-23.

[5]Harris Z.Distributional structure[J].Word，1954，10（23）：146-162.

[6]Firth J R.A synopsis of linguistic theory 1930-1955[J].Studies in Linguistic Analysis（Oxford：Philological Society）：1-32.

[7]Lai S W，Liu K，Xu L H，et al.How to generate a good word embedding? [J].IEEE Intelligent Systems，2016，31（6）：5-14.

[8]Lin D K，Pantel P.DIRT，discovery of inference rules from text[C]//Proceedings of Knowledge Discovery and Data Mining，2001：323-328.

[9]Turney P D.The latent relation mapping engine：algorithm and experiments[J].Journal of Artificial Intelligence Research，2008，33：615-655.

[10]Turney P D，Pantel P.From frequency to meaning：wector space models of semantics[J].Journal of Artificial Intelligence Research，2010，37：141-188.

[11]Salton G，Wong A，Yang C S.A vector space model for automatic indexing[J].Communications of the ACM，1975，18（11）：613-620.

[12]Deerwester S，Dumais S，Landauer T，et al.Harshman.Indexing by latent semantic analysis[J].Journal of the American Society for Information Science，1990 41（6）：391-407.

[13]Sparck-Jones K.A statistical interpretation of term specificity and its application in retrieval[J].Journal of Documentation，1972，28（1）：11-21.

[14]Blei D M，Ng A Y，Jordan M I.Latent dirichlet allocation[J].Journal of Machine Learning Research，2003，3：993-1022.

[15]Murphy B，Talukdar P，Mitchell T.Learning effective and interpretable semantic models using non-negative sparse embedding[C]//Proceedings of International Conference on Computational Linguistics，2012：1933-1950.

[16]Lee DD，Seung S.Learning the parts of objects by nonnegative matrix factorization[J].Nature，1999，401：788-791.

[17]Brown P，Vincent J，Pietra D，et al.Class-based n-gram models of natural language[J].Computational Linguistics，1992，18（4）：467-479.

[18]Pennington J，Socher R，Manning C.Glove：global vectors for word representation[C].//Proceedings of Empirical Methods in Natural Language Processing，2014：1532-1543.

[19]Lin D，Pantel P.DIRT，discovery of inference rules from text[C]//Proceedings of Knowledge Discovery and Data Mining，2001：323-328.

[20]Turney PD.The latent relation mapping engine：algorithm and experiments[J].Journal of Artificial Intelligence Research，2008，33：615-655.

[21]Bollegala D，Maehara T，Kawarabayashi K.Embedding semantic relations into word representations[C]//Proceedings of International Joint Conference on Artificial Intelligence，2015：1222-1228.

[22]Liu Y，Liu Z Y，Chua T S，et al.Topical word embeddings[C]//Proceedings of Association for the Advancement of Artificial Intelligence，2015：2418-2424.

[23]Pecina P，Schlesinger P.Combining association measures for collocation extraction[C]//Proceedings of Meeting of the Association for Computational Linguistics，2006：651-658.

[24]Church K，Hanks P.Word association norms，mutual information，and lexicography[C]//Proceedings of the 27th Annual Conference of the Association of Computational Linguistics，1989：76-83.

[25]Turian J P，Ratinov L A，Bengio Y.Word representations：a simple and general method for semi-supervised learning[C]//Proceedings of Meeting of the Association for Computational Linguistics，2010：384-394.

[26]Bengio Y，Ducharme R，Vincent P，et al.A neural probabilistic language model[J].Journal of Machine Learning Research，2003，3：1137-1155.

[27]Mnih A，Hinton G E.A scalable hierarchical distributed language model[C]//Proceedings of Neural Information Processing Systems，2008：1081-1088.

[28]Mikolov T，Kombrink S，Burget L，et al.Extensions of recurrent neural network language model[C]//Proceedings of International Conference on Acoustics，Speech，and Signal Processing，2011：5528-5531.

[29]Mikolov T，Zweig G.Context dependent recurrent neural network language model[C]//Proceedings of Spoken Language Technology Workshop，2012：234-239.

[30]Mikolov T，Sutskever I，Chen K，et al.Distributed representations of words and phrases and their compositionality[C].Proceedings of Neural Information Processing Systems，2013：3111-3119.

[31]Mikolov T，Chen K，Corrado G，et al.Efficient estimation of word representations in vector space[J].The Computing Research Repository，2013：1301.3781.

[32]Collobert R，Weston J.Fast semantic extraction using a novel neural network architecture[C]//Proceedings of Meeting of the Association for Computational Linguistics，2007.

[33]Levy O，Goldberg Y.Linguistic regularities in sparse and explicit word representations[J].Computational Natural Language Learning，2014：171-180.

[34]Li Y T，Xu L I，Tian F，et al.Word embedding revisited：a new representation learning and explicit matrix factorization perspective[C]//Proceedings of International Joint Conference on Artificial Intelligence，2015：3650-3656.

[35]Mnih A，Kavukcuoglu K.Learning word embeddings efficiently with noise-contrastive estimation[C]//Proceedings of Neural Information Processing Systems，2013：2265-2273.

[36]Tversky A.Features of similarity[J].Psychological Review，1977，84（4）.

[37]Kiela D，Hill F，Clark S.Specializing word embeddings for similarity or relatedness[C]//Proceedings of Empirical Methods in Natural Language Processing，2015：2044-2048.

[38]Faruqui M，Dodge J，Jauhar K S，et al.Retrofitting word vectors to semantic lexicons[C]//Proceedings of North American Chapter of the Association for Computational Linguistics，2015：1606-1615.

[39]Liu Q，Jiang H，Wei S，et al.Learning semantic word embeddings based on ordinal knowledge constraints[C]//Proceedings of Meeting of the Association for Computational Linguistics，2015：1501-1511.

[40]Chen Z G，Lin W，Chen Q，et al.Revisiting word embedding for contrasting meaning[C]//Proceedings of Meeting of the Association for Computational Linguistics，2015：106-115.

[41]Cohen W W，Schapire R E，Singer Y.Learning to order things[C]//Proceedings of Neural Information Processing Systems，1997：451-457.

[42]Mohammad S，Dorr B J，Hirst G.Computing word-pair antonymy[C]//Proceedings of Empirical Methods in Natural Language Processing，2008：982-991.

[43]Zhang J W，Salwen J，Glass M R，et al.Word semantic representations using bayesian probabilistic tensor factorization[C]//Proceedings of Empirical Methods in Natural Language Processing，2014：1522-1531.

[44]Rothe S，Schütze H.AutoExtend：extending word embeddings to embeddings for synsets and lexemes[C]//Proceedings of Meeting of the Association for Computational Linguistics，2015，1：1793-1803.

[45]Iacobacci I，Pilehvar M T，Navigli R.Sensembed：Learning sense embeddings for word and relational similarity[C]//Proceedings of Meeting of the Association for Computational Linguistics，2015：95-105.

[46]Navigli R，Ponzetto S P.BabelNet：the automatic construction，evaluation and application of a widecoverage multilingual semantic network[J].Artificial Intelligence，2012，193：217-250.

[47]Moro A，Raganato A，Navigli R.Entity linking meets word sense disambiguation：a unified approach[J].Transactions of the Association for Computational Linguistics，2014，2：231-244.

[48]Faruqui M，Dyer C.Improving Vector Space Word Representations Using Multilingual Correlation[C]//Proceedings of Conference of the European Chapter of the Association for Computational Linguistics，2014：462-471.

[49]Hermann K M，Blunsom P.Multilingual Distributed Representations without Word Alignment [DB/OL][2019-09-24].http：//arxiv.org/pdf/1312.6173.pdf.

[50]Hill F，Cho K，Jean S，et al.Embedding Word Similarity with Neural Machine Translation[J].The Computing Research Repository，2014，1412.6448.

[51]Schnabel T，Labutov I，Mimno D M，et al.Evaluation methods for unsupervised word embeddings[C]//Proceedings of Empirical Methods in Natural Language Processing，2015：298-307.

[52]Köhn A.What's in an embedding?analyzing word embeddings through multilingual evaluation[C]//Proceedings of Empirical Methods in Natural Language Processing，2015：2067-2073.

[53]Zhou G Y，He TT，Zhao J，et al.Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering[C]//Proceedings of Meeting of the Association for Computational Linguistics，2015，1：250-259.

[54]Zhou G Y，Zhou Y，He T T，et al.Learning semantic representation with neural networks for community question answering retrieval[J].Knowledge-Based Systems.2015，93：75-83.

[55]Yang M C，Lee D J，Park S Y，et al.Knowledge-based question answering using the semantic embedding space[J].Expert Systems with Applications，2015，42（23）：9086-9104.

[56]Zhang K，Wu W，Wang F，et al.Learning Distributed representations of data in community question answering for question retrieval[J].Web Search and Data Mining，2016：533-542.

[57]Dong H L，Wang J，Lin H F，et al.Predicting best answerers for new questions：an approach leveraging distributed representations of words in community question answering[J].Frontier of Computer Science and Technology，2015：13-18.

[58]Mikolov T，Le Q V，Sutskever I.Exploiting similarities among languages for machine translation[J].The Computing Research Repository，2013，1309.4168　.

[59]Zhang J J，Liu S J，Li M，et al.Bilingually-constrained phrase embeddings for machine translation[C]//Proceedings of Meeting of the Association for Computational Linguistics，2014，1：111-121.

[60]Le Q V，Mikolov T.Distributed representations of sentences and documents[C]//Proceedings of International Conference on Machine Learning，2014，1188-1196.

[61]Kusner M J，Sun Y，Kolkin N I，et al.From word embeddings to document distances[C]//Proceedings of International Conference on Machine Learning，2015：957-966.

[62]Sordoni A，Bengio Y，Nie J Y.Learning concept embeddings for query expansion by quantum entropy minimization[C]//Proceedings of Association for the Advancement of Artificial Intelligence，2014：1586-1592.

[63]Liu X H，Bouchoucha A，Sordoni A，et al.Compact aspect embedding for diversified query expansions[C]//Proceedings of Association for the Advancement of Artificial Intelligence，2014：115-121.

[64]Luo H Y，Liu Z Y，Luan H B，et al.Online learning of interpretable word embeddings[C]//Proceedings of Empirical Methods in Natural Language Processing，2015：1687-1692.

[65]Zhang W，Zhang K，Gu P，et al.Multi-view embedding learning for incompletely labeled data[C]//Proceedings of International Joint Conference on Artificial Intelligence，2013：1910-1916.

[66]Hyland S L，Karaletsos T，Rätsch G.A generative model of words and relationships from multiple sources[C]//Proceedings of Association for the Advancement of Artificial Intelligence，2016：2622-2629.

[67]Yang L，Sun M S.Improved learning of Chinese word embeddings with semantic knowledge[J].Chinese Computational Linguistics，2015：15-25.

[68]Sun Y，Lin L，Yang N，et al.Radical-enhanced Chinese character embedding[C]//Proceedings of International Conference on Neural Information Processing，2014：279-286.

[69]Bordes A，Usunier N，Garcia-Duran A，et al.Translating embeddings for modeling multi-relational data [C]//Proceedings of NIPS.2013：2787-2795.

[70]Dettmers T，Minervini P，Stenetorp P，et al.Convolutional 2D Knowledge Graph Embeddings[C]//Proceedings of national conference on artificial intelligence，2018：1811-1818.