[1] Harris Z S. Distributional Structure. Word, 1954, 10: 146-162 doi: 10.1080/00437956.1954.11659520
[2] Palangi H, Deng L, Shen Y, Gao J, He X, Chen J, Song X, Ward R. Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016, 24: 694-707 doi: 10.1109/TASLP.2016.2520371
[3] Le Q, Mikolov T. Distributed Representations of Sentences and Documents. In: Proceedings of the 31st International Conference on Machine Learning. New York, NY, USA: ACM, 2014. 1188-1196
[4] Liu Y, Liu Z, Chua T S, Sun M. Topical Word Embeddings. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2015. 2418-2424
[5] Mikolov T, Corrado G, Chen K, Dean J. Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv: 1301.3781, 2015. http://arxiv.org/abs/1301.3781
[6] Wang Z, Zhao K, Wang H, Dean J. Query Understanding through Knowledge-Based Conceptualization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. San Francisco, CA, USA: Morgan Kaufmann, 2015. 3264-3270
[7] Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv: 1409.0473, 2015. http://arxiv.org/abs/1409.0473
[8] Lin Y, Shen S, Liu Z, Luan H, Sun M. Neural Relation Extraction with Selective Attention over Instances. In: Proceedings of the 54th Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. 2124-2133
[9] Wang Y, Huang H, Feng C, Zhou Q, Gu J, Gao X. CSE: Conceptual Sentence Embeddings based on Attention Model. In: Proceedings of the 54th Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. 505-515
[10] Rayner K. Eye movements in Reading and Information Processing: 20 Years of Research. Psychological Bulletin, 1998, 124: 372-422 doi: 10.1037/0033-2909.124.3.372
[11] Nilsson M, Nivre J. Learning where to look: Modeling eye movements in reading. In: Proceedings of the 13th Conference on Computational Natural Language Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009. 93-101
[12] Lai S, Xu L, Liu K, Zhao J. Recurrent Convolutional Neural Networks for Text Classification. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI, 2015. 2267-2273
[13] Kenter T, Borisov A, De Rijke M. Siamese CBOW: Optimizing Word Embeddings for Sentence Representations. In: Proceedings of the 54th Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. 941-951
[14] Xiong C, Merity S, Socher R. Dynamic Memory Networks for Visual and Textual Question Answering. In: Proceedings of the 33rd International Conference on Machine Learning. New York, NY. USA: ACM, 2016. 1230-1239
[15] Yu J. Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. 236-246
[16] Yong Z, Meng J E, Ning W, Pratama M. Attention Pooling-based Convolutional Neural Network for Sentence Modelling. Information Sciences, 2016, 373: 388-403 doi: 10.1016/j.ins.2016.08.084
[17] Wang S, Zhang J, Zong C. Learning sentence representation with guidance of human attention. In: Proceedings of IJCAI International Joint Conference on Artificial Intelligence. San Francisco, CA, USA: Morgan Kaufmann, 2017. 4137-4143
[18] Lang Z, Gu X, Zhou Q, Xu T. Combining statistics-based and CNN-based information for sentence classification. In: Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence. New York, NY. USA: IEEE, 2017. 1012-1018
[19] Wieting J, Gimpel K. Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. 2078-2088
[20] Nicosia M, Moschitti A. Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information. In: Proceedings of the 21st Conference on Computational Natural Language Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. 260-270
[21] Peng W, Wang J, Zhao B, Wang L. Identification of Protein Complexes Using Weighted PageRank-Nibble Algorithm and Core-Attachment Structure. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2015, 12: 179-192 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=abeae9028ba365cfcf0b6aadc978a058
[22] Hua W, Wang Z, Wang H, Zheng K, Zhou X. Short text understanding through lexical-semantic analysis. In: Proceedings of the 31st IEEE International Conference on Data Engineering. New York, NY. USA: IEEE, 2015. 495-506
[23] Song Y, Wang H. Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence. San Francisco, CA, USA: Morgan Kaufmann, 2015. 3820-3826
[24] Wang F, Wang Z, Li Z, Wen J R. Concept-based Short Text Classification and Ranking. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York, NY. USA: ACM, 2014. 1069-1078
[25] Wang Y, Huang H, Feng C. Query Expansion Based on a Feedback Concept Model for Microblog Retrieval. In: Proceedings of the 26th International Conference on World Wide Web. New York, NY. USA: ACM, 2017. 559-568
[26] Wu W, Li H, Wang H, Zhu K Q. Probase: A probabilistic taxonomy for text understanding. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. New York, NY. USA: ACM, 2012. 481-492
[27] Yu Z, Wang H, Lin X, Wang M. Understanding Short Texts through Semantic Enrichment and Hashing. IEEE Transactions on Knowledge and Data Engineering, 2016, 28: 566-579 doi: 10.1109/TKDE.2015.2485224
[28] Itti L, Koch C, Niebur E. A Model of Saliency Based Visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20: 1254-1259 doi: 10.1109/34.730558
[29] Hahn M, Keller F. Modeling Human Reading with Neural Attention. Psychological Bulletin, 2016, 85: 618-627 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Arxiv000001371070
[30] Narayanan S, Jurafsky D. A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing. Advances in Neural Information Processing Systems, 2001, 14: 59-65 http://cn.bing.com/academic/profile?id=295791b42c4db2c7f0cea418567b841e&encoded=0&v=paper_preview&mkt=zh-cn
[31] Demberg V, Keller F. Data from eye-tracking corpora as evidence for theories of syntactic processing complexity. Cognition, 2016, 109: 193-210 http://cn.bing.com/academic/profile?id=8f52c0a66bab4fbea159c968896ec065&encoded=0&v=paper_preview&mkt=zh-cn
[32] Barrett M, Sogaard A. Reading behavior predicts syntactic categories. In: Proceedings of the 2015 SIGNLL Conference on Computational Natural Language Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2015. 345-349
[33] Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed Representations of Words and Phrases and their Compositionality. In: Proceedings of the 27th Annual Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2013. 1-9
[34] Fernandez-Carbajales V, García M A, MartU.S.A.nez J M. Visual attention based on a joint perceptual space of color and brightness for improved video tracking. Pattern Recognit, 2016, 60: 571-584
[35] Attneave F. Applications of information theory to psychology: a summary of basic concepts, methods, and results. American Journal of Psychology, 1961, 74(2): 319-324 doi: 10.2307/1419430
[36] Hale J. A probabilistic earley parser as a psycholinguistic model. In: Proceedings of the 2nd meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2001. 1-8
[37] Ounis I, Macdonald C, Lin J. Overview of the trec-2011 microblog track. In: Proceedings of the 2011 Text REtrieval Conference. Gaithersburg, MD, USA: NIST, 2001. 1-9
[38] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993-1022 http://d.old.wanfangdata.com.cn/Periodical/jsjyy201306024
[39] Fan R E, Chang K W, Hsieh C J, Wang X R, Lin C J. LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research, 2008, 9: 1871-1874 http://d.old.wanfangdata.com.cn/Periodical/dzjs201506001