Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (3): 291-297.doi: 10.11896/jsjkx.220700146

• Artificial Intelligence • Previous Articles     Next Articles

Employing Gated Mechanism to Incorporate Multi-features into Chinese Event Coreference Resolution

HUAN Zhigang1,2, JIANG Guoquan2, ZHANG Yujian1, LIU Liu2,3, LIU Shanshan2   

  1. 1 School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China
    2 The Sixty-third Research Institute,National University of Defense Technology,Nanjing 210007,China
    3 School of Information Engineering,Suqian University,Suqian,Jiangsu,223800,China
  • Received:2022-07-14 Revised:2022-11-04 Online:2023-03-15 Published:2023-03-15
  • About author:HUAN Zhigang,born in 1997,postgra-duate,is a student member of China Computer Federation.His main research interests include knowledge graph and natural language processing.
    JIANG Guoquan,born in 1978,associate research fellow,master,is a member of China Computer Federation.His main research interests include equipment data engineering and knowledge graph.
  • Supported by:
    General Support from China Postdoctoral Science Foundation(2021MD703983) and Scientific Research Program of National University of Defense Technology(ZK20-46).

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Abstract: Event coreference resolution is the basis of many natural language processing tasks,aiming to identify event mentions in text that refer to the same real event.Since Chinese grammar is much more complex than English,the method of capturing English text features is not effective in Chinese event corefe-rence resolution.To solve the within-document Chinese event corefe-rence,a gated mechanism neural network(GMNN) is proposed.In view of Chinese characteristics with subject omission and loose structure,event attributes are introduced as symbolic features.On this basis,a novel gated mechanism is proposed,which fine-tunes the symbolic feature vector,filters the noise in the symbolic features,extracts useful information in a specific context,and improves the coreference events recognition rate.Experimental results on the ACE2005 Chinese dataset show that the perfor-mance of GMNN improves by 2.66,which effectively improves the effect of Chinese event coreference resolution.

Key words: Chinese event coreference resolution, Gated mechanism, Neural network, Pre-trained language models, Symbolic features

CLC Number: 

  • TP391
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