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Context-Aware Heterogeneous Graph Interactive Learning for Document-Level Event Extraction

delete2026-01-01
delete0
PRE
AI
Y
Yang Liu
F
Fumin Guo
C
Cheng Hu
F
Fangfang Yuan
Cong Cao 封面图
Cong Cao (Cong Cao)
L
Lei Zhang *
DOI：10.1007/978-981-95-3456-2_6delete
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摘要

摘要

En 中文
文档级事件抽取专注于从非结构化文档中提取结构化的事件记录。由于文档中复杂的语义关系，现有方法在表示或利用上下文及实体信息方面存在局限性，导致事件抽取准确率下降。本文提出一种轻量级的端到端框架，通过异构图交互和通用上下文感知融合来弥补这一缺陷。首先，为捕捉文档内的复杂语义关系，我们采用异构图卷积网络模拟不同粒度间的信息交互。其次，在论元组合抽取阶段，我们设计了一种融合实体信息和通用上下文的策略，以提高实体关联预测的准确率并减少误差传播。在公开数据集上的实验表明，我们的轻量级模型在单事件和整体评估中均优于其他方法。
Keyword:
event extraction
heterogeneous graph interaction
common context-aware information
entity relevance

期刊

A
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2025, PT II
IF:
0
论文数:
22
被引数:
0

机构

I
institute of information engineering, cas
学者数:
477
论文数: 469
被引数: 0
H
henan university
学者数:
2.3W
论文数: 1.3W
被引数: 20
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
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引用论文

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