Joint Event Extraction based on Skip-Window Convolutional Neural Networks

摘要：

　　Traditional approaches to the task of ACE event extraction are either the joint model with elaborately designed features which may lead to generalization and data-sparsity problems,or the word-embedding model based on a twostage,multi-class classification architecture,which suffers from error propagation since event triggers and arguments are predicted in isolation.This paper proposes a novel event-extraction method that not only extracts triggers and arguments simultaneously,but also adopts a framework based on convolutional neural networks(CNNs)to extract features automatically.However,CNNs can only capture sentence-level features,so we propose the skip-window convolution neu-ral networks(S-CNNs)to extract global structured features,which effectively capture the global dependencies of every token in the sentence.The experimental results show that our approach outperforms other state-of-the-art methods.

作者: Zhengkuan Zhang Weiran Xu Qianqian Chen

作者单位: Automation School of Beijing University of Posts and Telecommunications No.10 Xitucheng Road,Haidian Beijing University of Posts and Telecommunications Emory University Apt 2,1535 N. Decatur Rd NE,Atlanta GA-30307

会议类型: 国际会议

会议名称: 第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)

会议地点: 昆明

会议语种:英文

页码: 1-12

在线出版日期: 2016-12-02（万方平台首次上网日期，不代表论文的发表时间）