ShanghaiTech University Knowledge Management System
Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field | |
2023 | |
会议录名称 | PROCEEDINGS OF THE ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS |
ISSN | 0736-587X |
卷号 | 1 |
页码 | 13695-13710 |
发表状态 | 已发表 |
摘要 | Prior works on joint Information Extraction (IE) typically model instance (e.g., event triggers, entities, roles, relations) interactions by representation enhancement, type dependencies scoring, or global decoding. We find that the previous models generally consider binary type dependency scoring of a pair of instances, and leverage local search such as beam search to approximate global solutions. To better integrate cross-instance interactions, in this work, we introduce a joint IE framework (CRFIE) that formulates joint IE as a high-order Conditional Random Field. Specifically, we design binary factors and ternary factors to directly model interactions between not only a pair of instances but also triplets. Then, these factors are utilized to jointly predict labels of all instances. To address the intractability problem of exact high-order inference, we incorporate a high-order neural decoder that is unfolded from a mean-field variational inference method, which achieves consistent learning and inference. The experimental results show that our approach achieves consistent improvements on three IE tasks compared with our baseline and prior work. © 2023 Association for Computational Linguistics. |
会议录编者/会议主办者 | Bloomberg Engineering ; et al. ; Google Research ; LIVEPERSON ; Meta ; Microsoft |
关键词 | Computational linguistics Image segmentation Information retrieval Random processes Beam search Binary factors Directly model Event trigger Global solutions High-order Higher-order Joint information Local search Random fields |
会议名称 | 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 |
会议地点 | Toronto, ON, Canada |
会议日期 | July 9, 2023 - July 14, 2023 |
收录类别 | EI |
语种 | 英语 |
出版者 | Association for Computational Linguistics (ACL) |
EI入藏号 | 20234314933846 |
EI主题词 | Decoding |
EI分类号 | 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory ; 723.2 Data Processing and Image Processing ; 903.3 Information Retrieval and Use ; 922.1 Probability Theory |
原始文献类型 | Conference article (CA) |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/345814 |
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 |
通讯作者 | Zheng, Zilong; Tu, Kewei |
作者单位 | 1.Beijing Institute for General Artificial Intelligence (BIGAI), Beijing, China; 2.ShanghaiTech University, Shanghai, China; 3.Beijing Jiaotong University, Beijing, China |
第一作者单位 | 上海科技大学 |
通讯作者单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Jia, Zixia,Yan, Zhaohui,Han, Wenjuan,et al. Modeling Instance Interactions for Joint Information Extraction with Neural High-Order Conditional Random Field[C]//Bloomberg Engineering, et al., Google Research, LIVEPERSON, Meta, Microsoft:Association for Computational Linguistics (ACL),2023:13695-13710. |
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