@inproceedings{97d33bdf31914dd2886ae0057df03b33,
title = "TriFusion: A Triple-View Fusion Framework for Document-Level Relation Extraction",
abstract = "Document-level Relation Extraction (DocRE) aims to identify relationship labels between multiple entities across an entire document, differing from traditional sentence-level extraction by requiring reasoning across multiple sentences. Existing methods rely on logical inference and entity-context relationships but often underutilize rich linguistic information in documents. This paper proposes a triple-view fusion framework (Dependency syntax relation context fusion, DSRC) to enhance syntax aggregation by integrating dependency trees. It leverages graph-structured syntax and dependency paths while addressing long-tail and multi-label challenges through relation relevance and entity completion. Evaluated on two benchmark datasets (DocRED, Re-DocRED), our method significantly outperforms state-of-the-art models.",
keywords = "Dependency Graph, Dynamic Gating, Entity Completion, Information Extraction",
author = "Peng Wang and Jianfei Zhang and Xinghan Lin and Yuanxin Ouyang and Wenge Rong",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE Smart World Congress, SWC 2025 ; Conference date: 18-08-2025 Through 22-08-2025",
year = "2025",
doi = "10.1109/SWC65939.2025.00196",
language = "英语",
series = "Proceedings - 2025 IEEE Smart World Congress, SWC 2025, 2025 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Scalable Computing and Communications",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1238--1243",
booktitle = "Proceedings - 2025 IEEE Smart World Congress, SWC 2025, 2025 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Scalable Computing and Communications",
address = "美国",
}