Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 337-343.doi: 10.11896/jsjkx.230800033

• Artificial Intelligence • Previous Articles     Next Articles

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

WU Hao1, ZHOU Gang1,2, LU Jicang1,2, LIU Hongbo1,2, CHEN Jing1,2   

  1. 1 School of Data and Target Engineering,Information Engineering University,Zhengzhou 450001,China
    2 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Received:2023-08-07 Revised:2023-12-13 Online:2024-10-15 Published:2024-10-11
  • About author:WU Hao,born in 1997,postgraduate.His main research interests include relation extraction and data mining.
    ZHOU Gang,born in 1974,Ph.D,professor.His main research interests include knowledge graph,mass data processing and knowledge grapgh.
  • Supported by:
    Science and Technology Research Program of the Department of Science and Technology of Henan Province(222102210081).

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Abstract: 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.

Key words: Relation extraction, Document level, Axial attention, Multi view, Multi-hop inference

CLC Number: 

  • TP391
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