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题名: Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
作者: Zheng SC(郑孙聪)1; Wang Feng(王峰)1; Bao Hongyun(包红云)1; Hao Yuexing(郝悦星)1; Peng Zhou1; Bo Xu1
出版日期: 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
英文摘要: 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
所属项目编号: 2015AA015402 ; 61602479
所属项目名称: National High Technology Research and Development Program of China (863 Program)
内容类型: 会议论文
URI标识: http://ir.ia.ac.cn/handle/173211/14344
Appears in Collections:数字内容技术与服务研究中心_超级计算大脑团队_会议论文

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作者单位: 1.Institute of Automation, Chinese Academy of Sciences

Recommended Citation:
Zheng SC,Feng Wang,Hongyun Bao,et al. Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme[C]. 见:The 55th annual meeting of the Association for Computational Linguistics (ACL). 加拿大温哥华. 2017-07-30.
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文件名: [17-ACL] Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme.pdf
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