南京大学学报（自然科学版）2023，Vol.59Issue(6)：1013-1022,10.DOI:10.13232/j.cnki.jnju.2023.06.011
基于BoBGSAL-Net的文档级实体关系抽取方法
Document-level entity relation extraction method based on BoBGSAL-NET
摘要
Abstract
The primary task of document-level entity relation extraction is to extract relationships among entities in a document.Compared to intra-sentence entity relation extraction,document-level entity relation extraction requires reasoning across multiple sentences in the document.To address the challenge of complex information interaction among different entities in the document,this paper proposes a Mixed Mention-Level Graph(MMLG)strategy for modeling intricate information interaction among different entities in the document,thereby enhancing the model's perception of document-level entity relations.Additionally,to handle the issue of relationship overlap within entity relations in documents,an Entity Relation Graph(ERG)module is constructed,incorporating a path reasoning mechanism that focuses on inferring and learning from multiple relationship paths among entities.This module enhances the accurate identification of entity and relation nodes at the mention level.By integrating the MMLG strategy and ERG module into the entity relation extraction model,this paper develops the BoBGSAL-Net(Based on Bipartite Graph Structure Aggregate Logic Network)model.Experimental evaluations are conducted on the publicly available DocRED dataset and the AlSiaRED dataset created by the authors'laboratory.The experimental results demonstrate the performance improvement of BoBGSAL-Net in document-level entity relation extraction tasks.Notably,the BoBGSAL-Net+BERT model achieves an F1 score of 66.04％in relation extraction tasks on the AlSiaRED dataset,showcasing a 4.4％overall performance improvement compared to other models.The model exhibits exceptional generalization capability,culminating in an optimal comprehensive performance.关键词
文档级实体关系抽取/混合提及级图/实体关系图/BoBGSAL-Net模型Key words
document-level entity relation extraction/mixed mention-level graph/entity relation graph/BoBGSAL-Net model分类
信息技术与安全科学引用本文复制引用
冯超文,吴瑞刚,温绍杰,刘英莉..基于BoBGSAL-Net的文档级实体关系抽取方法[J].南京大学学报（自然科学版）,2023,59(6):1013-1022,10.基金项目
国家自然科学基金(52061020,61971208),云南计算机技术应用重点实验室开放基金(2020103),云南省重大科技专项计划项目(202302AG050009) （52061020,61971208）
