Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 249-253.doi: 10.11896/jsjkx.200300156

• Artificial Intelligence • Previous Articles     Next Articles

Chinese Event Detection with Joint Representation of Characters and Words

WU Fan, ZHU Pei-pei, WANG Zhong-qing, LI Pei-feng, ZHU Qiao-ming   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2020-06-24 Online:2021-04-15 Published:2021-04-09
  • About author:WU Fan,born in 1996,postgraduate.His main research interests include natu-ral language processing and so on.(944721805@qq.com)
    ZHU Qiao-ming,born in 1963,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include natural language processing and so on.
  • Supported by:
    National Natural Science Foundation of China(61836007,61806137,61772354) and Natural Science Foundation of Jiangsu Province(18KJB520043).

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Abstract: As a sub-task of event extraction,event detection is a research hotspot in information extraction.It plays an important role in many NLP applications,such as knowledge graph,question answering and reading comprehension.Different with English character,a Chinese character can be regarded as single-character word and has its certain meaning.Moreover,there are specific structures in Chinese words.Therefore,this paper proposes a Chinese event detection model based on graph convolution neural network,called JRCW-GCN(Joint Representation of Characters and Words by Graph Convolution Neural Network),which integrates Chinese character and word representation.It uses the latest BERT and Transformer to encode the semantic information of words and characters respectively,and then uses the relationship between words and characters to construct the corresponding edges.Finally,it uses the graph convolution model to detect Chinese events by integrating the semantic information in Chinese character and word level.The experimental results on the ACE2005 Chinese corpus show that the performance of our model outperforms the state-of-the-art models.

Key words: Chinese event detection, Graph convolution, Joint representation

CLC Number: 

  • TP391
[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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