Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (8): 157-162.doi: 10.11896/jsjkx.220700161

• Artificial Intelligence • Previous Articles     Next Articles

Study on Enhanced Entity Representation for Document-level Relation Extraction

DING Xiaoyao1, ZHOU Gang1,2, LU Jicang1,2, CHEN Jing1,2   

  1. 1 PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,China
    2 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Received:2022-07-18 Revised:2023-05-24 Online:2023-08-15 Published:2023-08-02
  • About author:DING Xiaoyao,born in 1990,Ph.D.His main research interests include relation extraction and knowledge graph.
    ZHOU Gang,born in 1974,Ph.D,professor.His main research interests include big data,knowledge graph and data mining.
  • Supported by:
    Natural Science Foundation of Henan Province,China(222300420590).

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Abstract: Document-level relation extraction is a hot and challenging issue in natural language processing.Graph-based model is one of the mainstream methods of document-level relation extraction.Although this method can effectively solve the long-distance dependency between entity nodes,it often fails to fully consider the additional information such as sentence context,document topic,entity to entity distance and entity to similarity when constructing nodes,resulting in low performance of relationship extraction.A document-level relation extraction model based on enhanced entity representation is proposed to solve this problem.Firstly,the original document is used as input to construct the basic document graph structure.Then,the graph neural network propagation mechanism is used to aggregate the information of adjacent nodes,and the sentence context and topic information related to entity relation prediction is integrated into the entity node representation of the primary document graph,to obtain an enhanced entity node representation.Finally,the graph model of the enhanced entity node is used to predict the entity relationship.Experimental results show that the performance of the proposed model in the document-level relation extraction task is better than that of the existing models,and has better interpretability.

Key words: ocument-level, Relation extraction, Entity representation, Graph-based model

CLC Number: 

  • TN929.5
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