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Document-level relation extraction with structural encoding and entity-pair-level information interaction

delete2025-04-01
delete0
PRE
AI
W
Wanlong Liu
Y
Yichen Xiao
S
Shaohuan Cheng
D
Dingyi Zeng
L
Li Zhou
W
Weishan Kong
C
Chen, Wenyu *
DOI：10.1016/j.eswa.2024.126099delete
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摘要

摘要

En 中文
文档级关系抽取专注于识别文档全局中实体对之间的关系。然而，当前主流方法主要存在以下两个缺点：(a) 将上下文编码阶段与图推理阶段分离；(b) 限制在实体提及之间的推理，从而忽略了上下文中的关系相关性。本文介绍了SE-REPI模型，该模型由结构增强编码（SE）模块和基于关系实体对级交互（REPI）模块组成。SE模块有效地将结构信息注入Transformer架构中，并将图推理阶段整合到编码阶段。REPI模块构建实体对级信息交互，能够有效捕获实体对之间的关系相关性。在四个公开的DocRE数据集DocRED、CRD、GDA和DWIE上的全面实验结果证明了与以往尖端方法相比，我们的模型在性能和效率上的优越性。进一步的实验分析揭示了我们方法的可解释性。
Keyword:
Relation extraction
Structural transformer
Information extraction
Natural Language Processing

期刊

Expert Systems with Applications 封面图
Expert Systems with Applications
IF:
7.5
论文数:
3.0W
被引数:
10.2W

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