Computer Science ›› 2020, Vol. 47 ›› Issue (3): 206-210.doi: 10.11896/jsjkx.190200265

• Artificial Intelligence • Previous Articles     Next Articles

Document-level Event Factuality Identification Method with Gated Convolution Networks

ZHANG Yun,LI Pei-feng,ZHU Qiao-ming   

  1. (School of Computer Sciences and Technology, Soochow University, Suzhou, Jiangsu 215006, China)
  • Received:2019-02-05 Online:2020-03-15 Published:2020-03-30
  • About author:ZHANG Yun,born in 1993,postgradua-te,is member of China Computer Fede-ration.His main research interests include natural language processing. LI Pei-feng,born in 1971,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation.His main research interests include natural language processing,and machine lear-ning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61836007, 61772354, 61773276).

Abstract: Event factuality represents the factual nature of events in texts,it describes whether an event is a fact,a possibility,or an impossible situation.Event factuality identification is the basis of many relative tasks,such as question-answer system and discourse understanding.However,most of the current researches of event factuality identification focus on the sentences level,and only a few aim at the document-level.Therefore,this paper proposed an approach of document-level event factuality identification (DEFI) with gated convolution network.It first uses gated convolution network to capture both the semantic information and the syntactic information from event sentences and syntactic path,and then uses the self-attention layer to capture the feature representation of the overall information that is more important for each sequence itself.Finally,it uses the above information to identify the document-level event factuality.Experimental results on both the Chinese and English corpus show that the proposed DEFI outperforms the baselines both on macro-F1 and micro-F1.In Chinese and English corpus,the macro-average F1 value increased by 2.3% and 4.4%,while the micro-average F1 value increased by 2.0% and 2.8%,respectively.The training speed of this method is also increased by three times.

Key words: Discourse understanding, Event factuality identification, Gated convolution network

CLC Number: 

  • TP391.1
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