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于东 等:中文文本蕴含类型及语块识别方法研究 3785
[19] Matsuyoshi S, Miyao Y, Shibata T, et al. Overview of the NTCIR-11 recognizing inference in text and validation (RITE-VAL) task.
In: Proc. of the 11th NII Test Collection for Information Retrieval Workshop. 2014. 223−232.
[20] Williams A, Nangia N, Bowman SR. A broad-coverage challenge corpus for sentence understanding through inference. arXiv
preprint arXiv:1704.05426, 2017.
[21] Demszky D, Guu K, Liang P. Transforming question answering datasets into natural language inference datasets. arXiv preprint
arXiv:1809.02922, 2018.
[22] https://github.com/blcunlp/CNLI
[23] https://github.com/liuhuanyong/ChineseTextualInference
[24] Ren H. Research on annotation of linguistic phenomena for Chinese text reasoning. Journal of Henan Institute of Science and
Technology, 2017,37(7):75−78 (in Chinese with English abstract).
[25] Bentivogli L, Cabrio E, Dagan I, et al. Building textual entailment specialized data sets: A methodology for isolating linguistic
phenomena relevant to inference. In: Proc. of the LREC 2010. 2010.
[26] De Marneffe MC, Rafferty AN, Manning CD. Finding contradictions in text. In: Proc. of the HLT, Association for Computational
Linguistics (ACL 2008). Columbus, 2008. 1039−1047.
[27] Iftene A. UAIC participation at RTE4. In: Proc. of the 1st Text Analysis Conf. (TAC). 2008. 35, 104, 105.
[28] MacCartney B, Manning CD. Natural logic and natural language inference. In: Proc. of the Computing Meaning. Dordrecht:
Springer-Verlag, 2014. 129−147.
[29] Wang S, Jiang J. Learning natural language inference with LSTM. arXiv preprint arXiv:1512.08849, 2015.
[30] Sammons M, Vydiswaran VGV, Vieira T, et al. Relation alignment for textual entailment recognition. In: Proc. of the Text
Analysis Conf. (TAC). 2009.
[31] Tsuchida M, Ishikawa K. IKOMA at TAC2011: A method for recognizing textual entailment using lexical-level and sentence
structure-level features. In: Proc. of the Text Analysis Conf. (TAC). 2011.
[32] Blunsom P, Camburu OM, Lukasiewicz T, et al. e-SNLI: Natural language inference with natural language explanations. arXiv
preprint arXiv: 1812.01193, 2018.
[33] Liu MF, Li Y, Ji DH. Event semantic feature based Chinese textual entailment recognition. Journal of Chinese Information
Processing, 2013,27(5):129−136 (in Chinese with English abstract).
[34] Tan YM, Liu SW, Lv XQ. CNN and BiLSTM based Chinese textual entailment recognition. Journal of Chinese Information
Processing, 2018,32(7):11−19 (in Chinese with English abstract).
[35] Jin TH, Jiang S, Yu D, et al. Chinese chunked-based heterogeneous entailment parser and boundary identification. Journal of
Chinese Information Processing, 2019,33(2):17−25 (in Chinese with English abstract).
[36] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proc. of the Neural Information Processing Systems (NIPS).
2017. 5998−6008.
[37] Schuster M, Paliwal KK. Bidirectional recurrent neural networks. IEEE Trans. on Signal Processing, 1997,45(11):2673−2681.
[38] Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures.
Neural Networks, 2005,18(5-6):602−610.
[39] Lafferty J, McCallum A, Pereira FCN. Conditional random fields: Probabilistic models for segmenting and labeling sequence data.
In: Proc. of the ICML. 2001. 282−289.
[40] Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.
01360, 2016.
附中文参考文献:
[1] 郭茂盛,张宇,刘挺.文本蕴含关系识别与知识获取研究进展及展望.计算机学报,2017,40(4):889−910. http://cjc.ict.ac.cn/online/
onlinepaper/gms-201745180721.pdf [doi: 10.11897/SP.J.1016.2017.00889]
[2] 李继民.国内外语块研究述评.山东外语教学,2011,32(5):17−23.
[24] 任函.面向汉语文本推理的语言现象标注规范研究.河南科技学院学报,2017,37(7):75−78.
[33] 刘茂福,李妍,姬东鸿.基于事件语义特征的中文文本蕴含识别.中文信息学报,2013,27(5):129−136.