Using dual-layer CRFs for event causal relation extraction

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概要

• 論文の詳細を見る

• 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%.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Liu Wei

  School of Computer Engineering & Science, Shanghai University

• Liu Wei

  School of Aeronautics and Astronautics, Shanghai Jiao Tong University

• Guo Qiang

  School of Computer Engineering and Science, Shanghai University

• Fu Jianfeng

  School of Mathematics & Information, Shanghai Lixin University of Commerce

• Liu Zongtian

  School of Computer Engineering & Science, Shanghai University

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