[1] CHEN D Q, BOLTON J, MANING C D. A thorough examination of the CNN/Daily Mail reading comprehension task[C]//Proceeding of the 2016 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:ACL, 2016:2359-2367. [2] 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.(LIU Z Y, SUN M S, LIN Y K, et al. Knowledge representation learning:a review[J]. Journal of Computer Research and Development, 2016, 53(2):247-261.) [3] TURNEY P D, PANTEL P. From frequency to meaning:vector space models of semantics[J]. Journal of Artificial Intelligence Research, 2010, 37(1):141-188. [4] WIDDOWS D. Semantic vector products:some initial investigations[C/OL]//Proceedings of the 2008 Second AAAI Symposium on Quantum Interaction.[2016-10-09]. http://www.puttypeg.net/papers/semantic-vector-products.pdf. [5] MARELLI M, BENTIVOGLI L, BARONI M, et al. Semeval-2014 Task 1:evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment[C]//Proceedings of the 2014 8th International Workshop on Semantic Evaluation. Stroudsburg, PA:ACL, 2014:1-8. [6] WIDDOWS D. Geometry and Meaning[M]. Stanford, CA:CSLI Publications, 2004:23-28. [7] MITCHELL J, LAPATA M. Vector based models of semantic composition[C]//Proceedings of the 2008 Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:ACL, 2008:236-244. [8] BLACOE W, LAPATA M. A comparison of vector-based representations for semantic composition[C]//Proceeding of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg, PA:ACL, 2012:546-556. [9] GUEVARA E. A regression model of adjective-noun compositionality in distributional semantics[C]//Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics. Stroudsburg, PA:ACL, 2010:33-37. [10] MITCHELL J, LAPATA M. Composition in distributional models of semantics[J]. Cognitive Science, 2010, 34(8):1388-1429. [11] SOCHER R, HUANG E, PENNINGTON J, et al. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection[C]//Proceedings of the 2011 International Conference on Neural Information Processing Systems. Cambridge, MA:MIT Press, 2011:801-809. [12] ZANZOTTO F M, KORKONTZELOS I, FALLUCCHI F, et al. Estimating linear models for compositional distributional semantics[C]//Proceedings of the 2010 23rd International Conference on Computational Linguistics. Stroudsburg, PA:ACL, 2010:1263-1271. [13] SOCHER R, HUVAL B, MANNING C D, et al. Semantic compositionality through recursive matrix-vector spaces[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural language Processing and Computational Natural Language Learning. Stroudsburg, PA:ACL, 2012:1201-1211. [14] GUEVARA E. A regression model of adjective-noun compositionality in distributional semantics[C]//Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics. Stroudsburg, PA:ACL, 2010:33-37. [15] BARONI M, ZAMPARELLI R. Nouns are vectors, adjectives are matrices:representing adjective-noun constructions in semantic space[C]//Proceedings the 2010 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:ACL, 2010:1183-1193. [16] PAPERNO D, PHAM N, BARONI M. A practical and linguistically-motivated approach to compositional distributional semantics[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA:ACL, 2014:90-99. [17] TAI K S, SOCHER R, MANNING C D. Improved semantic representations from tree-structured long short-term memory networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg, PA:ACL, 2015:1556-1566. [18] ZAREMBA W, SUTSKEVER I. Learning to execute[EB/OL].[2016-10-09]. http://www.cs.nyu.edu/~zaremba/docs/Learning%20to%20Execute.pdf. [19] 王智强,李茹,梁吉业,等.基于汉语篇章框架语义分析的阅读理解问答研究[J].计算机学报,2016,39(4):795-807.(WANG Z Q, LI R, LIANG J Y, et al. Research on question answering for reading comprehension based on Chinese discourse frame semantic parsing[J]. Chinese Journal of Computers, 2016, 39(4):795-807.) [20] MIKOLOV T, CHEN K,CORRADO G, et al. Efficient estimation of word representations in vector space[EB/OL].[2016-10-09]. https://core.ac.uk/download/pdf/24794691.pdf. [21] 张志昌,张宇,刘挺,等.基于浅层语义树核的阅读理解答案句抽取[J].中文信息学报,2008,22(1):80-86.(ZHANG Z C, ZHANG Y, LIU T, et al. Answer sentence extraction of reading comprehension based on shallow semantic tree kernel[J]. Journal of Chinese Information Processing, 2008, 22(1):80-86.) [22] 朱征宇,孙俊华.改进的基于《知网》的词汇语义相似度计算[J].计算机应用,2013,33(8):2276-2279.(ZHU Z Y, SUN J H. Improved vocabulary semantic similarity calculation based on HowNet[J]. Journal of Computer Applications, 2013, 33(8):2276-2279.) |