Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Zheng SC(郑孙聪); Feng Wang; Hongyun Bao; Yuexing Hao; Peng Zhou; Bo Xu
2017
会议名称The 55th annual meeting of the Association for Computational Linguistics (ACL)
会议日期2017-07-30
会议地点加拿大温哥华
所属项目编号2015AA015402 ; 61602479
摘要Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-toend models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What’s more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.
收录类别EI
资助项目National High Technology Research and Development Program of China (863 Program)
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/14344
专题数字内容技术与服务研究中心_听觉模型与认知计算
作者单位Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Zheng SC,Feng Wang,Hongyun Bao,et al. Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme[C],2017.
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