A Convolution BiLSTM Neural Network Model for Chinese Event Extraction

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原文链接

NETL
Springer Nature

外文摘要：Chinese event extraction is a challenging task in information extraction。 Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools。 In this paper， we first come up with the language specific issue in Chinese event extraction， and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features。 Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling。

外文关键词：

Event extractionNeural networkChinese language processing

作者：

Ying Zeng、Honghui Yang、Yansong Feng、Zheng Wang、Dongyan Zhao

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作者单位：

Institute of Computer Science and Technology, Peking University, Beijing, People's Republic of China

School of Computing and Communications, Lancaster University, Lancaster, UK

会议名称：

International conference on computer processing of oriental languages;CCF conference on natural language processing and Chinese computing

会议地点：

Kunming(CN)

会议母体文献：

Natural language understanding and intelligent applications

页码：

275-287

出版时间：

2016

DOI：

10.1007/978-3-319-50496-4_23