[1] 孟红霞,白学军,闫国利,等.词边界信息对读者阅读歧义短语时眼跳策略的影响[J].心理科学,2015,38(4):770-776.MENG H X, BAI X J,YAN G L, et al. The effect of word boundary information on the saccade strategy upon reading the spatially ambiguous words[J].Journal of Psychological Science,2015,38(4):770-776. (in Chinese)
[2] FISHMAN G A, BIRCH D G, HOLDER G E, et al. Electrophysiologic testing in disorders of the retina, optic nerve, and visual pathway[M]. 2nd ed. San Francisco:The Foundation of the American Academy of Ophthalmology, 2001.
[3] RAYNER K. Eye movements in reading and information processing:20 years of research[J]. Psychological Bulletin, 1998, 124(3):372-422.
[4] RADACH R, MCCONKIE G W. Determinants of fixation positions in words during reading[M]//UNDERWOOD G. Eye guidance in reading and scene perception. Oxford, England:Elsevier Science Ltd., 1998:77-100.
[5] CLIFTON JR C, FERREIRA F, HENDERSON J M, et al. Eye movements in reading and information processing:Keith Rayner's 40 year legacy[J]. Journal of Memory and Language, 2016, 86:1-19.
[6] FRISSON S, HARVEY D R, STAUB A. No prediction error cost in reading:Evidence from eye movements[J]. Journal of Memory and Language, 2017, 95:200-214.
[7] KUPERBERG G R, JAEGER T F. What do we mean by prediction in language comprehension?[J]. Language, Cognition and Neuroscience, 2016, 31(1):32-59.
[8] LUKE S G, CHRISTIANSON K. Limits on lexical prediction during reading[J]. Cognitive Psychology, 2016, 88:22-60.
[9] REICHLE E D. Computational models of reading:A primer[J]. Language and Linguistics Compass, 2015, 9(7):271-284.
[10] SLATTERY T J, YATES M. Word skipping:Effects of word length, predictability, spelling and reading skill[J]. The Quarterly Journal of Experimental Psychology, 2017. DOI:10.1080/17470218.2017.1310264.
[11] 苏衡, 刘志方, 曹立人. 中文阅读预视加工中的词频和预测性效应及其对词切分的启示:基于眼动的证据[J]. 心理学报, 2016, 48(6):625-636.SU H, LIU Z F, CAO L R. The effects of word frequency and word predictability in preview and their implications for word segmentation in Chinese reading:Evidence from eye movements[J]. Acta Psychologica Sinica, 2016, 48(6):625-636. (in Chinese)
[12] REICHLE E D, RAYNER K, POLLATSEK A. The E-Z reader model of eye-movement control in reading:Comparisons to other models[J]. Behavioral and Brain Sciences, 2003, 26(4):445-476.
[13] ENGBERT R, NUTHMANN A, RICHTER E M, et al. SWIFT:A dynamical model of saccade generation during reading[J]. Psychological Review, 2005, 112(4):777-813.
[14] NILSSON M, NIVRE J. Learning where to look:Modeling eye movements in reading[C]//Proceedings of the 13th Conference on Computational Natural Language Learning. Boulder, Colorado:Association for Computational Linguistics, 2009:93-101.
[15] NILSSON M, NIVRE J. Towards a data-driven model of eye movement control in reading[C]//Proceedings of 2010 Workshop on Cognitive Modeling and Computational Linguistics. Uppsala, Sweden:Association for Computational Linguistics, 2010:63-71.
[16] MATTHIES F, SØ GAARD A. With blinkers on:Robust prediction of eye movements across readers[C]//Proceedings of 2013 Conference on Empirical Methods in Natural Language Processing. Seattle, Washington, USA:Association for Computational Linguistics, 2013:803-807.
[17] LANDWEHR N, ARZT S, SCHEFFER T, et al. A model of individual differences in gaze control during reading[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. Doha, Qatar:Association for Computational Linguistics,2014:1810-1815.
[18] HARA T, MOCHIHASHI D, KANO Y, et al. Predicting word fixations in text with a CRF model for capturing general reading strategies among readers[C]//Proceedings of the 1st Workshop on Eye-Tracking and Natural Language Processing. Mumbai, India:The COLING 2012 Organizing Committee, 2012:55-70.
[19] MOCH B N, KOMARUDIN K, SUSILO M S. Development of eye fixation points prediction model from eye tracking data using neural network[J]. International Journal of Technology, 2017, 8(6):1082-1088.
[20] HOU Y, LI Z, WANG P, et al. Skeleton optical spectra-based action recognition using convolutional neural networks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(3):807-811.
[21] COLLOBERT R, WESTON J, BOTTOU L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12:2493-2537.
[22] GOLDBERG Y. A primer on neural network models for natural language processing[J]. Journal of Artificial Intelligence Research, 2016, 57:345-420.
[23] DAT N D, DAT N D, TRAN V T N, et al. Fuzzy C-means for english sentiment classification in a distributed system[J]. Applied Intelligence, 2017, 46(3):717-738.
[24] HUANG M L, QIAN Q, ZHU X Y. Encoding syntactic knowledge in neural networks for sentiment classification[J]. ACM Transactions on Information Systems (TOIS), 2017, 35(3):26-33.
[25] 张宇,张鹏远,颜永红. 基于注意力LSTM和多任务学习的远场语音识别[J]. 清华大学学报(自然科学版), 2018,58(3),249-253. ZHANG Y, ZHANG P Y, YAN Y H. Long short-term memory with attention and multitask learning for distant speech recognition[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(3), 249-253. (in Chinese)
[26] 张雪英,牛溥华,高帆.基于DNN-LSTM的VAD算法[J]. 清华大学学报(自然科学版), 2018,58(5):509-515.ZHANG X Y, NIU P H, GAO F. DNN-LSTM based VAD algorithm[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(5):509-515. (in Chinese)
[27] DYER C, BALLESTEROS M, LING W, et al. Transition-based dependency parsing with stack long short-term memory[C]//Proceedings of the 53rd Annual Meeting of the Association for Com-putational Linguistics and the 7th International Joint Conference on Natural Language Processing. Beijing, China:Association for Computational Linguistics, 2015:321-332.
[28] GREFF K, SRIVASTAVA R K, KOUTNíK J, et al. LSTM:A search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(10):2222-2232.
[29] HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[J/OL]. (2015-08-09)[2018-09-10]. https://arxiv.org/abs/1508.01991v1.
[30] LUKE S G, CHRISTIANSON K. The Provo Corpus:A large eye-tracking corpus with predictability norms[J]. Behavior Research Methods, 2018, 50(2):826-833.
[31] KENNEDY A, PYNTE J, MURRAY W S, et al. Frequency and predictability effects in the Dundee Corpus:An eye movement analysis[J]. Quarterly Journal of Experimental Psychology, 2013, 66(3):601-618.
[32] YU A W, LEE H, LE Q V. Learning to skim text[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Ancouver, Canada:Association for Computational Linguistics, 2017:1880-1890.
[33] PASCANU R, MIKOLOV T, BENGIO Y. On the difficulty of training recurrent neural networks[C]//Proceedings of the 30th International Conference on International Conference on Machine Learning. Atlanta, USA:JMLR.org, 2012:Ⅲ-1310-Ⅲ-1318.
[34] ZEILER M D. ADADELTA:An adaptive learning rate method[J/OL]. (2012-12-22)[2018-09-10] http://cn.arxiv.org/abs/1212.5701.
[35] KINGMA D P, BA J. Adam:A method for stochastic optimization[J/OL]. (2014-12-22)[2018-09-10]. https://arxiv.org/abs/1412.6980.
[36] DAUPHIN Y N, DE VRIES H, BENGIO Y. Equilibrated adaptive learning rates for non-convex optimization[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal, Canada:MIT Press, 2015:1504-1512.
