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An Empirical Study of Pipeline vs. Joint Approaches to Entity and Relation Extraction
2022-11-20
会议录名称THE 2ND CONFERENCE OF THE ASIA-PACIFIC CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 12TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (AACL-IJCNLP 2022)
发表状态已发表
摘要

The Entity and Relation Extraction (ERE) task includes two basic sub-tasks: Named Entity Recognition and Relation Extraction. In the last several years, much work focused on joint approaches for the common perception that the pipeline approach suffers from the error propagation problem. Recent work reconsiders the pipeline scheme and shows that it can produce comparable results. To systematically study the pros and cons of these two schemes. We design and test eight pipeline and joint approaches to the ERE task. We find that with the same span representation methods, the best joint approach still outperforms the best pipeline model, but improperly designed joint approaches may have poor performance. We hope our work could shed some light on the pipeline-vs-joint debate of the ERE task and inspire further research

会议名称the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (AACL-IJCNLP 2022)
会议日期2022-11
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/284202
专题信息科学与技术学院_博士生
信息科学与技术学院_PI研究组_屠可伟组
共同第一作者Jia, Zixia
通讯作者Tu, Kewei
作者单位
1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
2.Shanghai Engineering Research Center of Intelligent Vision and Imaging
第一作者单位信息科学与技术学院
通讯作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
推荐引用方式
GB/T 7714
Yan, Zhaohui,Jia, Zixia,Tu, Kewei. An Empirical Study of Pipeline vs. Joint Approaches to Entity and Relation Extraction[C],2022.
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