Research Article
Self-organizing incremental and graph convolution neural network for English implicit discourse relation recognition
@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}, volume={9}, number={36}, 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
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.
Copyright © 2021 Yubo Geng et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
