基于多关系视图轴向注意力的文档级关系抽取  

Document-level Relation Extraction Based on Multi-relation View Axial Attention

作　　者：吴皓 周刚 卢记仓 刘洪波 陈静[1,2] WU Hao;ZHOU Gang;LU Jicang;LIU Hongbo;and CHEN Jing(School of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China;State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China)

机构地区：[1]战略支援部队信息工程大学数据与目标工程学院,郑州450001 [2]数学工程与先进计算国家重点实验室,郑州450001

出　　处：《计算机科学》2024年第10期337-343,共7页Computer Science

基　　金：河南省科技攻关项目(222102210081)。

摘　　要：文档级关系抽取旨在从文档中提取多个实体之间的关系。针对现有工作在不同关系类型的条件下,对于实体间的多跳推理能力受限的问题,提出了一种基于多关系视图轴向注意力的文档级关系抽取模型。该模型将依据实体间的关系类型构建多视图的邻接矩阵,并基于该多视图的邻接矩阵进行多跳推理。基于两个文档级关系抽取基准数据集GDA和DocRED分别进行实验,结果表明,所提模型在生物数据集GDA上的F1指标达到85.7%,性能明显优于基线模型;在DocRED数据集上也能够有效捕获实体间的多跳关系。Document-level relationship extraction aims to extract relationships between multiple entities from documents.To address the limited multi-hop reasoning capacity of existing methods for establishing connections between entities with different relationship types,this paper propose a document-level relationship extraction model based on multi-relation view axial attention.The model will construct a multi-view adjacency matrix based on the relationship types between entities,and use it to perform multi-hop reasoning.In order to evaluate the proposed model’s performance,two benchmark datasets for document-level relationship extraction,namely GDA and DocRED are used in this study.The experimental results demonstrate that the F1 metric achieves 85.7%on the biological dataset GDA,significantly surpassing the baseline model’s performance.Moreover,the proposed model proves effective in capturing the multi-hop relationships among entities in the DocRED dataset.

关 键 词：关系抽取 文档级 轴向注意力 多视图 多跳推理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]