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Volume 50 Issue 9
Sep.  2024
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Journal of Beijing University of Aeronautics and Astronautics  > 2024  >  50(9): 2763-2771.
WU T X，JI X，WANG H G，et al. Relation extraction based on fusion of graph structure and sequence features[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2763-2771 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0706
Citation: WU T X，JI X，WANG H G，et al. Relation extraction based on fusion of graph structure and sequence features[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2763-2771 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0706
shu

Relation extraction based on fusion of graph structure and sequence features

doi: 10.13700/j.bh.1001-5965.2022.0706
  • 1.

    Big Data Center of State Grid Corporation of China，Beijing 100052，China

  • 2.

    School of Computer Science and Engineering，Beihang University，Beijing 100191，China

Funds:  Big Data Center of State Grid Corporation of China Technology Project (52999021N005)
More Information
  • Corresponding author: E-mail：tongxin-wu@sgcc.com.cn
  • Received Date: 11 Aug 2022
  • Accepted Date: 04 Oct 2022
    • Available Online: 14 Nov 2022
  • Publish Date: 07 Nov 2022
    Abstract

  • Relation extraction is an important task for natural language processing applications. Most of the existing relation extraction methods mainly predict the relation based on language sequence features or structure information of sentences, which fails to effectively reflect the internal structure and features of the relation between entities. In this paper, a relation extraction model fusing graph structure and sequence feature information in sentences was proposed. The model used an attention-based graph convolutional neural network (GCN) to learn the structure information of sentences and utilized bi-directional long short-term memory (BiLSTM) to learn the sequence semantics. The relation was classified by considering the two features through the attention mechanism. Extensive experiments were conducted on a public dataset and a manually constructed dataset, which demonstrated the priority of the proposed model.

     

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