Event Causal Relation Extraction Based on Cascaded Conditional Random Fields
FU Jian-Feng1,2, LIU Zong-Tian2, LIU Wei2, ZHOU Wen2
1.School of Mathematics and Information,Shanghai Lixin University of Commerce,Shanghai 201620 2.School of Computer Engineering Science,Shanghai University,Shanghai 200072
Abstract：Traditional methods for event causal relation extraction covered only part of the explicit causal relation in the text. A method for event causal relation extraction is presented based on Cascaded Conditional Random Fields. The method casts the problem of event causal relation extraction as the labeling of event sequence. The Cascaded (Dual-layer) Conditional Random Fields is employed to label the causal relation of event sequence. The first layer of the Cascaded Conditional Random Fields 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 this 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%.
付剑锋，刘宗田，刘炜，周文. 基于层叠条件随机场的事件因果关系抽取[J]. 模式识别与人工智能, 2011, 24(4): 567-573.
FU Jian-Feng, LIU Zong-Tian, LIU Wei, ZHOU Wen. Event Causal Relation Extraction Based on Cascaded Conditional Random Fields. , 2011, 24(4): 567-573.
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2011, Vol. 24 
