sis 18: e29

Research Article

Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition

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  • @ARTICLE{10.4108/eai.22-11-2021.172215,
        author={Yubo Geng},
        title={Self-organizing incremental and graph convolution  neural network for English implicit discourse relation  recognition},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={11},
        keywords={English discourse relation recognition, self-organizing incremental, graph convolution neural network, BERT},
        doi={10.4108/eai.22-11-2021.172215}
    }
    
  • Yubo Geng
    Year: 2021
    Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition
    SIS
    EAI
    DOI: 10.4108/eai.22-11-2021.172215
Yubo Geng1,*
  • 1: School of English Language, Anhui International Studies University, Hefei, 230000 China
*Contact email: byoungholee@qq.com

Abstract

Implicit discourse relation recognition is a sub-task of discourse relation recognition, which is challenging because it is difficult to learn the argument representation with rich semantic information and interactive information. To solve this problem, this paper proposes a self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition. The method adopts the preliminary training language model BERT (Bidirectional Encoder Representation from Transformers) coding argument for argument. A classification model based on self-organizing incremental and graph convolutional neural network is constructed to obtain the argument representation which is helpful for English implicit discourse relation recognition. The experimental results show that the proposed method is superior to the benchmark model in terms of contingency relations and expansion relations.