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Document-level relation extraction with entity type constraints

delete2025-12-11
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PRE
AI
R
Ridong Han
彭涛 cover
彭涛 (Tao Peng)
B
Beibei Zhu
H
Haijia Bi
J
Jiayu Han
X
Xinzheng Xu
L
Liu, Lu
DOI:10.1016/j.neunet.2025.108469delete
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Abstract

Abstract

En 中文
• Using relation correlations to solve both long-tail and multi-label problems. • Capturing relation correlations with entity type constraints from the global and local perspectives. • Type-constrained graph is constructed in the global perspective. • Type-constrained loss is designed in the local perspective. • Experiments confirm the effectiveness of proposed model in solving long-tail problem and multi-label problem.

Journal

Neural Networks cover
Neural Networks
IF:
6.3
Papers:
7.8K
Citations:
3.0W

Organization

L
Liaoning Normal University
Scholars:
4.2K
Papers: 2.5K
Citations: 2.1K
U
University of Washington
Scholars:
8.0W
Papers: 7.0W
Citations: 12.5W
C
China University of Mining and Technology
Scholars:
8.6K
Papers: 3.1K
Citations: 3.1W
J
Jilin University
Scholars:
8.6W
Papers: 5.5W
Citations: 8.9K
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