Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (12): 229-235.doi: 10.11896/jsjkx.230500010

• Artificial Intelligence • Previous Articles     Next Articles

Method of Document Level Relation Extraction Based on Fusion of Relational Transfer Information Using Double Graph

KOU Jiaying, ZHAO Weidong, LIU Xianhui   

  1. College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2023-05-04 Revised:2023-08-30 Online:2023-12-15 Published:2023-12-07
  • About author:KOU Jiaying,born in 1999,postgra-duate.Her main research interests include relation extraction and information extraction.
    LIU Xianhui,born in 1979,Ph.D,associate professor.His man research intere-sts include machine learning,data mi-ning and big data,networked manufacturing.
  • Supported by:
    National Key Research and Development Program of China(2020YFB1709303).

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Abstract: Document-level relation extraction refers to the extraction of entities and their relations from long paragraphs of unstructured text.Compared to traditional sentence-level relation extraction,document-level relation extraction requires the integration of contextual information from multiple sentences and logical reasoning to extract relation triples.In response to the current limitations of document-level relation extraction methods,such as incomplete modeling of document semantic information and li-mited extraction effects,a double-graph document-level relation extraction method that integrates relational transfer information is proposed.Interactions mentioned between different sentences are introduced into the path construction through the transitivity of relational information,and the interaction information mentioned in the same sentence as well as the coreference information between mentions are used to construct the path set between mention nodes,so as to improve the completeness of document modeling.A mention hierarchy graph aggregation network is constructed using the path set and mention nodes,and a document semantic information model is established.After the information iteration of the graph convolutional network(GCN),the information of different mention nodes of the same entity is fused to form entity node,which constitutes an entity-level graph reasoning network.Finally,logical inference is performed based on the path information between entity graph nodes to extract the relation between entities.The proposed model is experimented on the public dataset DocRED(document-level relation extraction dataset),and the experimental results show a improvement of 1.2(F1) compared to the baseline model,which proves the effectiveness of the proposed method.

Key words: Document relation extraction, Relational transfer, Graph convolutional network, Logical reasoning

CLC Number: 

  • TP391
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