1 王瑞琴, 杨小明, 楼俊钢. 词汇语义相关性度量研究[J]. 情报学报, 2016, 35(4): 389-404. 2 MikolovT, YihW, ZweigG. Linguistic regularities in continuous space word representations[C]// Proceedings of the Conference of the North American Chapter of the ACL. Stroudsburg: Association for Computational Linguistics, 2013: 746-751. 3 MikolovT, ChenK, CorradoG, et al. Efficient estimation of word representations in vector space[OL]. [2018-09-12]. https://arxiv.org/pdf/1301.3781.pdf 4 MitchellJ, LapataM. Composition in distributional models of semantics[J]. Cognitive Science, 2010, 34(8): 1388-1429. 5 IyyerM, ManjunathaV, Boyd-GraberJ, et al. Deep unordered composition rivals syntactic methods for text classification[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 1681-1691. 6 HermannK M, BlunsomP. The role of syntax in vector space models of compositional semantics[C]// Proceedings of the 51st Annual Meeting of the ACL. Stroudsburg: Association for Computational Linguistics, 2013, 1: 894-904. 7 KartsaklisD. Compositional operators in distributional semantics[J]. Springer Science Reviews, 2014, 2(1-2): 161-177. 8 SocherR, PerelyginA, WuJ, et al. Recursive deep models for semantic compositionality over a sentiment treebank[C]// Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2013: 1631-1642. 9 KalchbrennerN, GrefenstetteE, BlunsomP. A convolutional neural network for modeling sentences[C]// Proceedings of the 52nd Annual Meeting of the ACL. Stroudsburg: Association for Computational Linguistics, 2014, 1: 655-665. 10 ShenD H, WangG Y, WangW L, et al. Baseline needs more love: On simple word-embedding-based models and associated pooling mechanisms[C]// Proceedings of the 56th Annual Meeting of the ACL. Stroudsburg: Association for Computational Linguistics, 2018. 11 FaruquiM, DodgeJ, JauharS K, et al. Retrofitting word vectors to semantic lexicons[C]// Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2015: 1606-1615. 12 SpeerR, Chin J, Havasi C. ConceptNet 5. 5: An open multilingual graph of general knowledge[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 4444-4451. 13 PilehvarM T, CollierN. Inducing embeddings for rare and unseen words by leveraging lexical resources[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 388-393. 14 Mrk?i?N, óSéaghdha D, ThomsonB, et al. Counter-fitting word vectors to linguistic constraints[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 142-148. 15 Mrk?i?N, Vuli?I, óSéaghdha D, et al. Semantic specialization of distributional word vector spaces using monolingual and cross-lingual constraints[J]. Transactions of the Association for Computational Linguistics, 2017, 5: 309-324. 16 YuM, DredzeM. Improving lexical embeddings with semantic knowledge[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2014: 545-550. 17 LiuQ, JiangH, WeiS, et al. Learning semantic word embeddings based on ordinal knowledge constraints[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 1501-1511. 18 NguyenK A, WaldeS S, VuN T. Integrating distributional lexical contrast into word embeddings for antonym–synonym distinction[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 454. 19 MorinF, BengioY. Hierarchical probabilistic neural network language model[C]// Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, 2005: 246-252. 20 SaltonG, WongA, YangC S. A vector space model for automatic indexing[J]. Communications of the ACM, 1975, 18(11): 613-620. 21 DeerwesterS, DumaisS T, FurnasG W, et al. Indexing by latent semantic analysis[J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407. 22 SchutzeH. Word space[J]. Advances in Neural Information Processing Systems, 1993, 22(6): 895-902. 23 DhillonP S, FosterD, UngarL. Multi-view learning of word embeddings via CCA[C]// Proceedings of the 24th International Conference on Neural Information Processing Systems. Granada: Curran Associates Inc, 2011: 199-207. 24 DhillonP S, RoduJ, FosterD P, et al. Two step CCA: a new spectral method for estimating vector models of words[C]// Proceedings of the International Conference on International Conference on Machine Learning, 2012: 67-74. 25 LeeD D, SeungH S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791. 26 HofmannT. Unsupervised learning by probabilistic latent semantic analysis[J]. Machine Learning, 2001, 42(1-2): 177-196. 27 BleiD M, NgA Y, JordanM I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022. 28 GaussierE, GoutteC. Relation between pLSA and NMF and implications[C]// Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2005: 601-602. 29 DingC, LiT, PengW. On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing[J]. Computational Statistics & Data Analysis, 2008, 52(8): 3913-3927. 30 BengioY, DucharmeR, VincentP, et al. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2003, 3(6): 1137-1155. 31 CollobertR, WestonJ. A unified architecture for natural language processing: deep neural networks with multitask learning[C]// Proceedings of the International Conference on Machine Learning. New York: ACM Press, 2008: 160-167. 32 MnihA, HintonG. Three new graphical models for statistical language modelling[C]// Proceedings of the 24th International Conference on Machine Learning. New York: ACM Press, 2007: 641-648. 33 MikolovT, KarafiátM, BurgetL, et al. Recurrent neural network based language model[C]// Proceedings of the 11th Annual Conference of the International Speech Communication Association, 2010: 1045-1048. 34 PenningtonJ, SocherR, ManningC. Glove: Global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1532-1543. 35 LevyO, GoldbergY. Neural word embedding as implicit matrix factorization[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2014: 2177-2185. 36 LiY, XuL, TianF, et al. Word embedding revisited: a new representation learning and explicit matrix factorization perspective[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 3650-3656. 37 GoldbergY. A primer on neural network models for natural language processing[J]. Journal of Artificial Intelligence Research, 2016, 57: 345-420. 38 KielaD, HillF, ClarkS. Specializing word embeddings for similarity or relatedness[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 2044-2048. 39 ChenX X, LiuZ Y, SunM S. A unified model for word sense representation and disambiguation[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1025-1035. 40 ChenT, XuR, HeY, et al. Improving distributed representation of word sense via WordNet gloss composition and context clustering[J]. Atmospheric Measurement Techniques, 2015, 4(3): 5211-5251. 41 GuoJ, CheW, WangH, et al. Learning sense-specific word embeddings by exploiting bilingual resources[C]// Proceedings of the 25th International Conference on Computational Linguistics, 2014: 497-507. 42 UpadhyayS, ChangK W, TaddyM, et al. Beyond bilingual: multi-sense word embeddings using multilingual context[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 101-110. 43 NeelakantanA, ShankarJ, PassosA, et al. Efficient non-parametric estimation of multiple embeddings perword in vector space[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1059-1069. 44 LiuY, LiuZ, ChuaT S, et al. Topical word embeddings[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 2418-2424. 45 NguyenD Q, NguyenD Q, ModiA, et al. A mixture model for learning multi-sense word embeddings[C]// Proceedings of the 6th Joint Conference on Lexical and Computational Semantics. Stroudsburg: Association for Computational Linguistics, 2017: 121-127. 46 LiJ W, JurafskyD. Do multi-sense embeddings improve natural language understanding?[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 1722-1732. 47 PilehvarM T, CollierN. Inducing embeddings for rare and unseen words by leveraging lexical resources[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 388-393. 48 LinzenT, DupouxE, GoldbergY. Assessing the ability of LSTMs to learn syntax-sensitive dependencies[J]. Transactions of the Association for Computational Linguistics, 2016, 4: 521-535. 49 CaoS S, LuW, ZhouJ, et al. Cw2vec: Learning Chinese word embeddings with stroke n-gram information[C]// Proceedings of the 32th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2018: 1-8. 50 LiY R, LiW J, SunF, et al. Component-enhanced Chinese character embeddings[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 829-834. 51 YinR C, WangQ, LiP, et al. Multi-granularity Chinese word embedding[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 981-986. 52 YuJ X, JianX, XinH, et al. Joint embeddings of Chinese words, characters, and fine-grained sub-character components[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2017: 286-291. 53 Vuli?I, Mrk?i?N, ReichartR, et al. Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017, 1: 56-68. 54 CotterellR, SchützeH. Joint semantic synthesis and morphological analysis of the derived word[J]. Transactions of the Association for Computational Linguistics, 2018, 6: 33-48. 55 ZouW Y, SocherR, CerD, et al. Bilingual word embeddings for phrase-based machine translation[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2013: 1393-1398. 56 XuK, WanX J. Towards a universal sentiment classifier in multiple languages[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2017: 511-520. 57 李志义, 黄子风, 许晓绵. 基于表示学习的跨模态检索模型与特征抽取研究综述[J]. 情报学报, 2018, 37(4): 422-435. 58 LazaridouA, BaroniM. Combining language and vision with a multimodal skip-gram model[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2015: 153-163. 59 KotturS, VedantamR, MouraJ M F, et al. VisualWord2Vec (vis-W2V): Learning visually grounded word embeddings using abstract scenes[C]// Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 4985-4994. 60 MaoJ, XuJ, JingK, et al. Training and evaluating multimodal word embeddings with large-scale web annotated images[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems, 2016: 442-450. 61 SuT R, LeeH Y. Learning Chinese word representations from glyphs of characters[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2017: 264-273. 62 ErkK. Supporting inferences in semantic space: representing words as regions in vector space[C]// Proceedings of the 13th Conference on Computational Natural Language Learning, 2009: 57-65. 63 ZhangJ W, SalwenJ, GlassM, et al. Word semantic representations using Bayesian probabilistic tensor factorization[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1522-1531. 64 VilnisL, McCallumA. Word representations via Gaussian embedding[C]// Proceedings of the International Conference on Learning Representations, 2015. 65 AthiwaratkunB, WilsonA G, AnandkumarA. Probabilistic fast text for multi-sense word embeddings[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018. 66 BarkanO. Bayesian neural word embedding[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 3135-3143. 67 AthiwaratkunB, WilsonA. Multimodal word distributions[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 1645-1656. 68 Bra?inskasA, HavrylovS, TitovI. Embedding words as distributions with a Bayesian Skip-Gram model[C]// Proceedings of the 27th International Conference on Computational Linguistics, 2018. 69 MelamudO, GoldbergerJ, DaganI. Context2vec: Learning generic context embedding with bidirectional LSTM[C]// Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2016: 51-61. 70 McCannB, BradburyJ, XiongC, et al. learned in translation: contextualized word vectors[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems, 2017: 6294-6305. 71 PetersM, AmmarW, BhagavatulaC, et al. Semi-supervised sequence tagging with bidirectional language models[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 1756-1765. 72 PetersM, NeumannM, IyyerM, et al. Deep contextualized word representations[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 2227-2237. 73 FuR J, GuoJ, QinB, et al. Learning semantic hierarchies: A continuous vector space approach[J]. ACM Transactions on Audio, Speech, and Language Processing, 2015, 23(3): 461-471. 74 ShwartzV, GoldbergY, DaganI. Improving hypernymy detection with an integrated path-based and distributional method[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 2389-2398. 75 NguyenK A, K?perM, im WaldeS S, et al. Hierarchical embeddings for hypernymy detection and directionality[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2017: 233-243. 76 XieR, YuanX, LiuZ, et al. Lexical sememe prediction via word embeddings and matrix factorization[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017: 4200-4206. 77 ZengX, YangC, TuC, et al. Chinese LIWC lexicon expansion via hierarchical classification of word embeddings with sememe attention[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2018. 78 BordesA, UsunierN, Garcia-DuranA, et al. Translating embeddings for modeling multi-relational data[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems, 2013: 2787-2795. 79 HamiltonW L, LeskovecJ, JurafskyD. Cultural shift or linguistic drift? Comparing two computational measures of semantic change[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 2116-2121. 80 AzarbonyadH, DehghaniM, BeelenK, et al. Words are malleable: Computing semantic shifts in political and media discourse[C]// Proceedings of the International Conference on Information and Knowledge Management, 2017: 1509-1518. 81 刘知远, 刘扬, 涂存超, 等. 词汇语义变化与社会变迁定量观测与分析[J]. 语言战略研究, 2016, 1(6): 47-54. 82 HellrichJ, BuechelS, HahnU. JESEME: A Website for exploring diachronic changes in word meaning and emotion[C]// Proceedings of the 27th International Conference on Computational Linguistics, 2018. 83 GargN, SchiebingerL, JurafskyD, et al. Word embeddings quantify 100 years of gender and ethnic stereotypes[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(16): E3635. 84 CaliskanA, BrysonJ J, NarayananA. Semantics derived automatically from language corpora contain human-like biases[J]. Science, 2016, 356(6334): 183-186. 85 BolukbasiT, ChangK W, ZouJ Y, et al. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems, 2016: 4349-4357. 86 KusnerM J, LoftusJ, RussellC, et al. Counterfactual fairness[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2017: 4066-4076. 87 PleissG, RaghavanM, WuF, et al. On fairness and calibration[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2017: 5680-5689. 88 蔡永明, 长青. 共词网络LDA模型的中文短文本主题分析[J]. 情报学报, 2018, 37(3): 305-317. 89 周清清, 章成志. 在线用户评论细粒度属性抽取[J]. 情报学报, 2017, 36(5): 484-493. 90 赵洪, 王芳. 理论术语抽取的深度学习模型及自训练算法研究[J]. 情报学报, 2018, 37(9): 923-938. 91 张晓娟. 利用嵌入方法实现个性化查询重构[J]. 情报学报, 2018, 37(6): 621-630. 92 张志毅, 张庆云. 柏拉图以来词义说的新审视[J]. 中国语文, 2000(2): 126-136, 190. 93 FirthJ R. A synopsis of linguistic theory 1930-55[J]. Studies in Linguistic Analysis, 1957, 41(4): 1-32. 94 HarrisZ S. Distributional structure[J]. WORD, 1954, 10(2-3): 146-162. 95 HuangE H, SocherR, ManningC D, et al. Improving word representations via global context and multiple word prototypes[C]// Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2012: 873-882. 96 XuY, LiuJ W, YangW, et al. Incorporating latent meanings of morphological compositions to enhance word embeddings[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 1232-1242. 97 AvrahamO, GoldbergY. The interplay of semantics and morphology in word embeddings[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 422-426. 98 BothaJ A, BlunsomP. Compositional morphology for word representations and language modeling[C]// Proceedings of the International Conference on Machine Learning, 2014: 1899-1907. 99 QiuS, CuiQ, BianJ, et al. Co-learning of word representations and morpheme representations[C]// Proceedings of the 25th International Conference on Computational Linguistics, 2014: 141-150. 100 SoricutR, OchF. Unsupervised morphology induction using word embeddings[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2015: 1627-1637. 101 SunF, GuoJ, LanY, et al. Inside out: two jointly predictive models for word representations and phrase representations[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 2821-2827. 102 CaoK, ReiM. A Joint model for word embedding and word morphology[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 18-26. 103 XuJ, LiuJ, ZhangL, et al. Improve Chinese word embeddings by exploiting internal structure[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 1041-1050. 104 LevyO, GoldbergY. Dependency-based word embeddings[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2014: 302-308. 105 刘永彬, 欧阳纯萍, 钟东来, 等. 基于非线性全局上下文的词嵌入[J]. 中国科学: 信息科学, 2015, 45(12): 1588-1599. 106 BansalM, GimpelK, LivescuK. Tailoring continuous word representations for dependency parsing[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2014: 809-815. 107 LingW, DyerC, BlackA W, et al. Two too simple adaptations of word2vec for syntax problems[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2015: 1299-1304. 108 ZhaoZ, LiuT, LiS, et al. Ngram2vec: Learning improved word representations from n-gram co-occurrence statistics[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2017: 244-253. 109 SantosC D, ZadroznyB. Learning character-level representations for part-of-speech tagging[C]// Proceedings of the 31st International Conference on Machine Learning, 2014: 1818-1826. 110 KimY, JerniteY, SontagD, et al. Character-aware neural language models[C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 2741-2749. 111 JozefowiczR, VinyalsO, SchusterM, et al. Exploring the limits of language modeling[OL]. [2018-09-20]. https://arxiv.org/pdf/1602.02410.pdf. 112 WietingJ, BansalM, GimpelK, et al. Charagram: Embedding words and sentences via character n-grams[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 1504-1515. 113 BojanowskiP, GraveE, JoulinA, et al. Enriching word vectors with subword information[J]. Transactions of the Association for Computational Linguistics, 2017, 5: 135-146. 114 ChenX, XuL, LiuZ, et al. Joint learning of character and word embeddings[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 1236-1242. 115 LevyO, GoldbergY, DaganI. Improving distributional similarity with lessons learned from word embeddings[J]. Transactions of the Association for Computational Linguistics, 2015, 3: 211-225. 116 MinkaT, LaffertyJ. Expectation-propagation for the generative aspect model[C]// Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, 2002: 352-359. 117 GriffithsT L, SteyversM. Finding scientific topics[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101(Suppl 1): 5228-5235. 118 牛奉高, 张亚宇. 基于共现潜在语义向量空间模型的语义核构建[J]. 情报学报, 2017, 36(8): 834-842. 119 LundK, BurgessC. Producing high-dimensional semantic spaces from lexical co-occurrence[J]. Behavior Research Methods, Instruments, & Computers, 1996, 28(2): 203-208. 120 DeanJ, GhemawatS. MapReduce: Simplified data processing on large clusters[J]. Communications of the ACM, 2008, 51(1): 107-113. 121 BuntineW. Variational extensions to EM and multinomial PCA[C]// Proceedings of the 13th European Conference on Machine Learning. Heidelberg: Springer, 2002: 23-34. 122 MikolovT, KombrinkS, BurgetL, et al. Extensions of recurrent neural network language model[C]// Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE, 2011: 5528-5531. 123 ElmanJ L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179-211. 124 WerbosP J. Back propagation through time: What it does and how to do it[J]. Proceedings of the IEEE, 1990, 78(10): 1550-1560. 125 王毅, 谢娟, 成颖. 结合LSTM和CNN混合架构的深度神经网络语言模型[J]. 情报学报, 2018, 37(2): 194-205. 126 GoodmanJ. Classes for fast maximum entropy training[C]// Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, 2001: 561-564. 127 MnihA, HintonG. A scalable hierarchical distributed language model[C]// Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing. New York: IEEE, 2008: 1081-1088. 128 MikolovT, SutskeverI, ChenK, et al. Distributed representations of words and phrases and their compositionality[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2013: 3111-3119. 129 ChenW, GrangierD, AuliM. Strategies for training large vocabulary neural language models[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 1975-1985. 130 BengioY, SénécalJ S. Quick Training of probabilistic neural nets by importance sampling[C]// Proceedings of the Conference on Artificial Intelligence and Statistics, 2003: 1-9. 131 BengioY, SenécalJ S. Adaptive importance sampling to accelerate training of a neural probabilistic language model[J]. IEEE Transactions on Neural Networks, 2008, 19(4): 713-722. 132 GutmannM, Hyv?rinenA. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models[C]// Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010: 297-304. 133 MnihA, KavukcuogluK. Learning word embeddings efficiently with noise-contrastive estimation[C]// Proceedings of the Conference on Advances in Neural Information Processing Systems, 2013: 2265-2273. 134 SchnabelT, LabutovI, MimnoD, et al. Evaluation methods for unsupervised word embeddings[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 298-307. 135 FinkelsteinL, GabrilovichE, MatiasY, et al. Placing search in context: The concept revisited[J]. ACM Transactions on Information Systems, 2002, 20(1): 116-131. 136 AgirreE, AlfonsecaE, HallK, et al. A Study on similarity and relatedness using distributional and WordNet based approaches[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2009: 19-27. 137 LuongM T, SocherR, ManningC D. Better word representations with recursive neural networks for morphology[C]// Proceedings of the 17th Conference on Computational Natural Language Learning, 2013: 104-113. 138 GerzD, Vuli?I, HillF, et al. SimVerb-3500: A large-scale evaluation set of verb similarity[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 2173-2182. 139 CiaramitaM, JohnsonM. Supersense tagging of unknown nouns in WordNet[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2003: 168-175. 140 TsvetkovY, FaruquiM, WangL, et al. Evaluation of word vector representations by subspace alignment[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2015: 2049-2054. 141 LiS, ZhaoZ, HuR, et al. Analogical reasoning on Chinese morphological and semantic relations[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018. 142 TurianJ, RatinovL, BengioY. Word representations: a simple and general method for semi-supervised learning[C]// Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2010: 384-394. 143 MillerS. Name tagging with word clusters and discriminative training[C]// Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2004: 337-342. 144 KooT, CarrerasX, CollinsM. Simple semi-supervised dependency parsing[C]// Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2008: 595-603. 145 BakarovA. A survey of word embeddings evaluation methods[OL]. [2018-09-25]. https://arxiv.org/pdf/1801.09536.pdf. 146 索绪尔. 索绪尔第三次普通语言学教程[M]. 上海: 上海人民出版社, 2007. 147 LevyO, GoldbergY. Linguistic regularities in sparse and explicit word representations[C]// Proceedings of the 18th Conference on Computational Natural Language Learning. Stroudsburg: Association for Computational Linguistics, 2014: 171-180. 148 LiJ, MonroeW, DanJ. Understanding neural networks through representation erasure[OL]. [2018-09-28]. https://arxiv.org/pdf/1612.08220v3.pdf. 149 BaroniM, DinuG, KruszewskiG. countDon t, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors[C]// Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2014: 238-247. 150 HowardJ, RuderS. Universal language model fine-tuning for text classification[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2018: 328-339. 151 DevlinJ, ChangM W, LeeK, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[OL]. [2018-10-10]. https://arxiv.org/pdf/1810.04805.pdf. 152 刘知远, 孙茂松, 林衍凯, 等. 知识表示学习研究进展[J]. 计算机研究与发展, 2016, 53(2): 247-261. 153 张金柱, 于文倩, 刘菁婕, 等. 基于网络表示学习的科研合作预测研究[J]. 情报学报, 2018, 37(2): 132-139.
