Using dual-layer CRFs for event causal relation extraction

IEICE Electronics Express

Jianfeng Fu¹  Zongtian Liu²  Qiang Guo²  Wei Liu² 

[1] School of Mathematics & Information, Shanghai Lixin University of Commerce;School of Computer Engineering & Science, Shanghai University

关键词: event causal relation;    event sequence;    dual-layer CRFs;    Conditional Random Fields;   

DOI  :  10.1587/elex.8.306

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(6)Cited-By(1)Traditional methods for event causal relation extraction covered only part of the explicit causal relation in text. This paper presents a method for event causal relation extraction by using dual-layer Conditional Random Fields (CRFs). The method casts the problem of event causal relation extraction as event sequence labeling and employs dual-layer CRFs model to label the causal relation of event sequence. The first layer of the CRFs model is used to label the semantic role of causal relation of the events, and then the output of the first layer is passed to the second layer for labeling the boundaries of the event causal relation. Experimental results show that our method not only covers each class of explicit event causal relation in the text, but also achieves good performance and the F-Measure of the overall performance arrives at 85.3%.

【 授权许可】

Unknown