Using Entity Relation to Improve Event Detection via Attention Mechanism

摘要：

　　Identifying event instance in texts plays a critical role in the field of Information Extraction(IE).The currently proposed methods that employ neural networks have successfully solve the problem to some extent,by encoding a series of linguistic features,such as lexicon,partof-speech and entity.However,so far,the entity relation hasnt yet been taken into consideration.In this paper,we propose a novel event extraction method to exploit relation information for event detection(ED),due to the potential relevance between entity relation and event type.Methodologically,we combine relation and those widely used features in an attention-based network with Bidirectional Long Short-term Memory(Bi-LSTM)units.In particular,we systematically investigate the effect of relation representation between entities.In addition,we also use different attention strategies in the model.Experimental results show that our approach outperforms other state-of-the-art methods.

关键词： Event detection Attention mechanisms Entity relation

作者: Jingli Zhang Wenxuan Zhou Yu Hong Jianmin Yao Min Zhang

作者单位: Computer Science and Technology,Soochow University,Suzhou,Jiangsu,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 171-183

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）