@inproceedings{c158dfa4e1b84b45a16a64078e232c61,
title = "KEoG: A knowledge-aware edge-oriented graph neural network for document-level relation extraction",
abstract = "Document-level relation extraction (RE) has attracted more and more attentions recently. Edge-oriented graph neural network (EoG) is a new neural network exhibiting greater potential than previous node-oriented graph neural networks for document-level RE. In this paper, we propose a novel EoG, called knowledge-aware edge-oriented GNN (KEoG) for document-level RE. In KEoG, we further introduce not only two types of nodes to represent documents and external knowledge respectively, but also soft F-Measure loss function to solve the inherent class imbalance problem in document-level RE. Experiments conducted on two document-level datasets show that KEoG outperforms other state-of-the-art methods for comparison on both intra-sentence and inter-sentence relation extractions, indicating that KEoG is an effective extension of EoG.",
keywords = "Edge-oriented GNN, FMeasure Loss Function, Relation Extraction",
author = "Tao Li and Weihua Peng and Qingcai Chen and Xiaolong Wang and Buzhou Tang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",
year = "2020",
month = dec,
day = "16",
doi = "10.1109/BIBM49941.2020.9313590",
language = "英语",
series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1740--1747",
editor = "Taesung Park and Young-Rae Cho and Hu, \{Xiaohua Tony\} and Illhoi Yoo and Woo, \{Hyun Goo\} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",
booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",
address = "美国",
}