Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (5): 189-200.doi: 10.11896/jsjkx.220400252

• Artificial Intelligence • Previous Articles     Next Articles

Review of Document-level Relation Extraction Techniques

ZHU Taojie1, LU Jicang1,2, ZHOU Gang1,2, DING Xiaoyao1, WANG Ling1, ZHU Xiubao1   

  1. 1 Information Engineering University,Zhengzhou 450001,China
    2 State Key Laboratory of Mathematical Engineering and Advanced Computing,Zhengzhou 450001,China
  • Received:2022-04-25 Revised:2022-07-29 Online:2023-05-15 Published:2023-05-06
  • About author:ZHU Taojie,born in 1995,postgraduate.His main research interests include knowledge graph and data mining.
    LU Jicang,born in 1985,Ph.D,associate professor.His main research interests include knowledge reasoning and social network analysis.
  • Supported by:
    Natural Science Foundation of Henan,China(222300420590).

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Abstract: Relation extraction(RE) is an essential direction of information extraction research,it gradually expanding from sentence to document-level.Compared with sentences,documents usually contain more relation facts,providing more information for knowledge base construction,information retrieval,and semantic analysis.However,document-level relation extraction is more complex and challenging,and there is currently a lack of systematic and comprehensive sorting and summary.To better promote the development of document-level relation extraction,this paper carries out a comprehensive and in-depth analysis of the existing technologies and methods.From the perspective of data preprocessing methods and core algorithms,it classifies the existing methods into three types,including tree-based,sequence-based,and graph-based.Based on this,Relation extraction by category analyzes and describes some typical methods,the latest progress and shortcomings.At the same time,it introduces some corpus,evaluation metrics and some typical methods.Finally,the existing problems in document-level relation extraction research are analyzed and summarized,and the possible future development trends and research directions are discussed.

Key words: Information extraction, Document-level relation extraction, Data preprocess, Datasets, Performance evaluation

CLC Number: 

  • TP391
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