Improving Event Extraction Using Online Learning Strategy

摘要：

　　The traditional mention-oriented model for event extraction cannot capture information of multiple event types in a sentence.To deal with this problem,we present a novel intensive model for event extraction task.Firstly,the model can filter out non-event sentences automatically by introducing some meaningful language features.Then,the model adopts an online learning strategy for event type ranking,which provides an alternative view of event type identification as multi-label classification so that we can achieve a predicted set of relevant event types for each sentence.The experimental evaluation verified that the model can improve the performance of the event extraction task.

作者: Jinxiu Chen

作者单位: School of Information Science and Engineering Xiamen university Xiamen,P.R.China 361005

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 602-606

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）