Implicit Causality Extraction Method Based on Event Action Direction

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.

Keywords

• causality|causal connectives|event extraction|event action|syntactic structure analysis