Document-Level Relation Extraction Method Based on Attention Semantic Enhancement

被引：0
|
作者：
Liu X. [1 ]
Wu W. [1 ]
Zhao W. [1 ,2 ]
Hou W. [1 ]
机构：
[1] College of Electronic and Information Engineering, Tongji University, Shanghai
[2] Shanghai Visual Perception and Intelligent Computing Engineering Technology Research Center, Shanghai
来源：
Tongji Daxue Xuebao/Journal of Tongji University | 2024年 / 52卷 / 05期
关键词：
attention mechanism; document-level relation extraction; focal loss; semantic enhancement;
D O I：
10.11908/j.issn.0253-374x.22503
中图分类号：
学科分类号：
摘要：
Document-level relation extraction aims to extract the relations between multiple entity pairs from a document，a task characterized by high complexity. This paper proposes a method for document-level relation extraction based on attention semantic enhancement to address challenges such as handling multiple entities，capturing relationship correlations， and dealing with imbalanced relationship distributions within documents. The method proposed facilitates the inference of relationships between entity pairs. Specifically，the data encoding module enhances the encoding strategy by incorporating additional entity information， capturing semantic features of the document through the encoding network， and generating an entity pair matrix. Subsequently，a U-Net network employing an attention gating mechanism is devised to capture local information and aggregate global information from entity pair matrices， thereby achieving semantic enhancement. Finally， this paper introduces an adaptive focal loss function to mitigate imbalanced relationship distributions. The Att-DocuNet model proposed is evaluated on four publicly available document-level relation extraction datasets （DocRED，CDR，GDA，and DWIE），yielding promising experimental results. © 2024 Science Press. All rights reserved.
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收藏
页码：822 / 828
页数：6
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