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

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.