Using Entity Relation to Improve Event Detection via Attention Mechanism
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.
Event detection Attention mechanisms Entity relation
Jingli Zhang Wenxuan Zhou Yu Hong Jianmin Yao Min Zhang
Computer Science and Technology,Soochow University,Suzhou,Jiangsu,China
国际会议
2018自然语言处理与中文计算国际会议(NLPCC2018)
呼和浩特
英文
171-183
2018-08-26(万方平台首次上网日期,不代表论文的发表时间)