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Entity-Dependency Memory-Enhanced Document-Level Relation Extraction

delete2026-01-01
delete0
PRE
AI
L
Li He
H
Hu Li *
J
Jianyong Duan
刘杰 封面图
刘杰 (Jie Liu)
H
Hao Wang
Q
Qing Zhang
DOI：10.1007/978-3-032-04630-7_12delete
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摘要

摘要

En 中文
文档理解中的文档级关系抽取旨在识别跨句的复杂实体关系，但现有方法在捕捉长距离实体依赖时往往面临挑战。为解决此问题，本文提出了一种基于实体依赖记忆增强（EMDRE）的文档级关系抽取模型。通过引入计算机视觉领域的记忆增强技术——标记图灵机（TTM），该模型有效提升了文档理解任务中长距离实体依赖的建模能力。该方法增强了记忆机制，显著提高了模型捕捉文档内复杂实体关系的能力。此外，为解决文档级关系抽取任务中的类别不平衡问题，本文提出了一种自适应排序优化策略，动态调整正负样本对模型训练的贡献，进一步提升了模型性能。实验结果表明，EMDRE模型在文档级关系抽取任务中优于现有基线模型，验证了其在文档理解中的有效性和创新性。
Keyword:
Document Understanding
Document-level Relation Extraction
Memory Enhancement

期刊

D
DOCUMENT ANALYSIS AND RECOGNITION-ICDAR 2025, PT V
IF:
0
论文数:
28
被引数:
0

机构

N
north china university of technology
学者数:
981
论文数: 415
被引数: 0
引用论文

引用论文

DocRED: A Large-Scale Document-Level Relation Extraction Dataset
errProceedings of the 57th Annual Meeting of the Association for Computational Linguistics
IF0
err2019-01-01
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Document-level relation extraction with three channels
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