基于层叠条件随机场的事件因果关系抽取  

Event Causal Relation Extraction Based on Cascaded Conditional Random Fields

作　　者：付剑锋[1,2] 刘宗田[2] 刘炜[2] 周文[2] 

机构地区：[1]上海立信会计学院数学与信息学院,上海201620 [2]上海大学计算机工程与科学学院,上海200027

出　　处：《模式识别与人工智能》2011年第4期567-573,共7页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60975033);上海市重点学科开放课题项目(No.J50103)资助

摘　　要：传统的事件因果关系抽取方法只能覆盖文本中的部分显式因果关系.针对这种不足,提出一种基于层叠条件随机场模型的事件因果关系抽取方法.该方法将事件因果关系的抽取问题转化为对事件序列的标注问题,采用层叠(两层)条件随机场标注出事件之间的因果关系.第一层条件随机场模型用于标注事件在因果关系中的语义角色,标注结果传递给第二层条件随机场模型用于识别因果关系的边界.实验表明,本文方法不仅可以覆盖文本中的各类显式因果关系,并且均能取得较好的抽取效果,总体抽取效果的F1值达到85.3%.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%.

关 键 词：事件因果关系 事件序列 层叠条件随机场 条件随机场模型 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]