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29-05-2023 | Regular Paper

Local discriminative graph convolutional networks for text classification

Authors: Bolin Wang, Yuanyuan Sun, Yonghe Chu, Changrong Min, Zhihao Yang, Hongfei Lin

Published in: Multimedia Systems | Issue 4/2023

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Abstract

Recently, graph convolutional networks (GCNs) has demonstrated great success in the text classification. However, the GCN only focuses on the fitness between the ground-truth labels and the predicted ones. Indeed, it ignores the local intra-class diversity and local inter-class similarity that is implicitly encoded by the graph, which is an important cue in machine learning field. In this paper, we propose a local discriminative graph convolutional network (LDGCN) to boost the performance of text classification. Different from the text GCN that minimize only the cross entropy loss, our proposed LDGCN is trained by optimizing a new discriminative objective function. So that, in the new LDGCN feature spaces, the texts from the same scene class are mapped closely to each other and the texts of different classes are mapped as farther apart as possible. So as to ensure that the features extracted by GCN have good discriminative ability, achieve the maximum separability of samples. Experimental results demonstrate its superiority against the baselines.

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Title: Local discriminative graph convolutional networks for text classification
Authors: Bolin Wang
Yuanyuan Sun
Yonghe Chu
Changrong Min
Zhihao Yang
Hongfei Lin
Publication date: 29-05-2023
Publisher: Springer Berlin Heidelberg
Published in: Multimedia Systems / Issue 4/2023
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI: https://doi.org/10.1007/s00530-023-01112-y

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