Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (3): 276-280.doi: 10.11896/jsjkx.211100249

• Artificial Intelligence • Previous Articles     Next Articles

Implicit Causality Extraction Method Based on Event Action Direction

MIU Feng1, WANG Ping2, LI Tai-yong2   

  1. 1 School of Artificial Intelligence and Law,Southwest University of Political Science&Law,Chongqing 401120,China
    2 School of Economic Information Engineering,Southwestern University of Finance and Economics,Chengdu 611130,China
  • Received:2021-11-24 Revised:2021-12-17 Online:2022-03-15 Published:2022-03-15
  • About author:MIU Feng,born in 1982,Ph.D,lecturer.His main research interests include NLP and financial intelligence.
  • Supported by:
    Humanities and Social Science Project from the Ministry of Education of China(19YJAZH047).

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Abstract: Extracting the causality between events can be applied to automatic question answering,knowledge extraction,common sense reasoning and so on.Due to the lack of obvious lexical features and the complex syntactic structure of Chinese,it is very difficult to extract implicit causality,which has become the bottleneck of the current research.In contrast,it is easy to extract expli-cit causality with high accuracy,and the logical causal relationship between events is stable.Therefore,an original method is proposed in this paper.Firstly,the extracted explicit causal event pairs are normalized to form the event direction,and then the event subject is generalized to form a standard set of matched causal event pairs.This set is used to extract implicit causal event pairs according to event similarity.In order to identify more implicit causality,a new causal connectives discovery algorithm is proposed.The experimental data crawling on NetEase Finance,Tencent Finance and Sina Finance show that the extraction precision is improved by 1.02% compared with the traditional method.

Key words: Causal connectives, Causality, Event action, Event extraction, Syntactic structure analysis

CLC Number: 

  • TP391.1
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