Joint Extraction of Multiple Relations and Entities by using a Hybrid Neural Network

摘要：

　　This paper proposes a novel end-to-end neural model to jointly extract entities and relations in a sentence.Unlike most exist-ing approaches,the proposed model uses a hybrid neural network to automatically learn sentence features and does not rely on any Natural Language Processing(NLP)tools,such as dependency parser.Our model is further capable of modeling multiple relations and their correspond-ing entity pairs simultaneously.Experiments on the CoNLL04 dataset demonstrate that our model using only word embeddings as input fea-tures achieves state-of-the-art performance.

关键词： Information Extraction Neural Networks

作者: Peng Zhou Suncong Zheng Jiaming Xu Zhenyu Qi Hongyun Bao Bo Xu

作者单位: Institute of Automation,Chinese Academy of Sciences;University of Chinese Academy of Sciences Institute of Automation,Chinese Academy of Sciences

会议类型: 国内会议

会议名称: 第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

会议地点: 南京

会议语种:英文

页码: 1-12

在线出版日期: 2017-10-13（万方平台首次上网日期，不代表论文的发表时间）