计算机科学 ›› 2021, Vol. 48 ›› Issue (4): 249-253.doi: 10.11896/jsjkx.200300156
吴凡, 朱培培, 王中卿, 李培峰, 朱巧明
WU Fan, ZHU Pei-pei, WANG Zhong-qing, LI Pei-feng, ZHU Qiao-ming
摘要: 事件检测作为事件抽取的一个子任务,是当前信息抽取的研究热点之一。它在构建知识图谱、问答系统的意图识别和阅读理解等应用中有着重要的作用。与英文字母不同,中文中的字在很多场合作为单字词具有特定的语义信息,且中文词语内部也存在特定的结构形式。根据中文的这一特点,文中提出了一种基于字词联合表示的图卷积模型JRCW-GCN(Joint Representation of Characters and Words by Graph Convolution Neural Network),用于中文事件检测。JRCW-GCN首先通过最新的BERT预训练语言模型以及Transformer模型分别编码字和词的语义信息,然后利用词和字之间的关系构建对应的边,最后使用图卷积模型同时融合字词级别的语义信息进行事件句中触发词的检测。在ACE2005中文语料库上的实验结果表明,JRCW-GCN的性能明显优于目前性能最好的基准模型。
中图分类号:
[1]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.<br /> [2]LI P,ZHU Q,DIAO H,et al.Joint modeling of trigger identification and event type determination in Chinese event extraction[C]//Proceedings of COLING 2012.2012:1635-1652.<br /> [3]LI P,ZHOU G,ZHU Q,et al.Employing compositional semantics and discourse consistency in Chinese event extraction[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.2012:1006-1016.<br /> [4]CHEN C,NG V.Joint modeling for chinese event extractionwith rich linguistic features[C]//Proceedings of COLING 2012.2012:529-544.<br /> [5]GRISHMAN R,WESTBROOK D,MEYERS A.Nyu’s english ace 2005 system description[J].ACE,2005,5.<br /> [6]JI H,GRISHMAN R.Refining event extraction through cross-document inference[C]//Proceedings of ACL-08:HLT.2008:254-262.<br /> [7]LIAO S,GRISHMAN R.Using document level cross-event inference to improve event extraction[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics,2010:789-797.<br /> [8]HONG Y,ZHANG J,MA B,et al.Using cross-entity inference to improve event extraction[C]//Proceedings of the 49thAnnualMeeting of the Association for Computational Linguistics:Human Language Technologies.Association for Computational Linguistics,2011:1127-1136.<br /> [9]COLLOBERT R,WESTON J,BOTTOU L,et al.Natural language processing(almost) from scratch[J].Journal of Machine Learning Research,2011,12(Aug):2493-2537.<br /> [10]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.<br /> [11]CHO K,VAN MERRIËNBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014.<br /> [12]LIU S,CHEN Y,LIU K,et al.Exploiting argument information to improve event detection via supervised attention mechanisms[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics.2017:1789-1798.<br /> [13]LIU S,CHEN Y,HE S,et al.Leveraging framenet to improve automatic event detection[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.2016:2134-2143.<br /> [14]LIU J,CHEN Y,LIU K,et al.Event detection via gated multilingual attention mechanism[C]//Thirty-Second AAAI Confe-rence on Artificial Intelligence.2018.<br /> [15]NGUYEN T H,GRISHMAN R.Graph convolutional networks with argument-aware pooling for event detection[C]//Thirty-Second AAAI Conference on Artificial Intelligence.2018.<br /> [16]ZENG Y,YANG H,FENG Y,et al.A convolution BiLSTM neural network model for Chinese event extraction[M]//Natural Language Understanding and Intelligent Applications.Springer,Cham,2016:275-287.<br /> [17]LIN H,LU Y,HAN X,et al.Nugget Proposal Networks forChinese Event Detection[J].arXiv:1805.00249,2018.<br /> [18]DING N,LI Z,LIU Z,et al.Event Detection with Trigger-Aware Lattice Neural Network[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Proces-sing and the 9th International Joint Conference on Natural Language Processing(EMNLP-IJCNLP).2019:347-356.<br /> [19]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is All you Need[C]//Neural Information Processing Systems.2017:5998-6008.<br /> [20]DEVLIN J,CHANG M W,LEE K,et al.Bert:Pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018.<br /> [21]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.<br /> [22]LI S,ZHAO Z,HU R,et al.Analogical reasoning on chinese morphological and semantic relations[J].arXiv:1805.06504,2018.<br /> [23] FENG X,QIN B,LIU T.A language-independent neural network for event detection[J].Science China Information Sciences,2018,61(9):092106. |
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