CASIA OpenIR  > 数字内容技术与服务研究中心  > 听觉模型与认知计算
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
Conference NameThe 55th annual meeting of the Association for Computational Linguistics (ACL)
Conference Date2017-07-30
Conference Place加拿大温哥华
Project Number2015AA015402 ; 61602479
AbstractJoint 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.
Indexed ByEI
Funding ProjectNational High Technology Research and Development Program of China (863 Program)
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14344
Collection数字内容技术与服务研究中心_听觉模型与认知计算
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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