Event Extraction via Bidirectional Long Short-Term Memory Tensor Neural Networks

摘要：

　　Traditional approaches to the task of ACE event extraction usually rely on complicated natural language processing(NLP)tools and elaborately designed features.Which suffer from error propagation of the existing tools and take a large amount of human effort.And nearly all of approaches extract each argument of an event separately without considering the interaction between candidate arguments.By contrast,we propose a novel event-extraction method,which aims to automatically extract valuable clues without using complicated NLP tools and predict all arguments of an event simultaneously.In our model,we exploit a context-aware word representation model based on Long Short-Term Memory Networks(LSTM)to capture the semantics of words from plain texts.In addition,we propose a tensor layer to explore the interaction between candidate arguments and predict all arguments simultaneously.The experimental results show that our approach significantly outperforms other state-of-the-art methods.

作者: Yubo Chen Shulin Liu Shizhu He Kang Liu Jun Zhao

作者单位: National Laboratory of Pattern Recognition Institute of Automation,Chinese Academy of Sciences,Beijing,100190,China

会议类型: 国内会议

会议名称: 第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

会议地点: 烟台

会议语种:英文

页码: 1-12

在线出版日期: 2016-10-14（万方平台首次上网日期，不代表论文的发表时间）