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Label-representative graph convolutional network for multi-label text classification

Label-representative graph convolutional network for multi-label text classification Multi-label text classification (MLTC) is the task that assigns each document to the most relevant subset of class labels. Previous works usually ignored the correlation and semantics of labels resulting in information loss. To deal with this problem, we propose a new model that explores label dependencies and semantics by using graph convolutional networks (GCN). Particularly, we introduce an efficient correlation matrix to model label correlation based on occurrence and co-occurrence probabilities. To enrich the semantic information of labels, we design a method to use external information from Wikipedia for label embeddings. Correlated label information learned from GCN is combined with fine-grained document representation generated from another sub-net for classification. Experimental results on three benchmark datasets show that our model outweighs prior state-of-the-art methods. Ablation studies also show several aspects of the proposed model. Our code is available at https://github.com/chiennv2000/LR-GCN. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Label-representative graph convolutional network for multi-label text classification

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References (38)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04106-x
Publisher site
See Article on Publisher Site

Abstract

Multi-label text classification (MLTC) is the task that assigns each document to the most relevant subset of class labels. Previous works usually ignored the correlation and semantics of labels resulting in information loss. To deal with this problem, we propose a new model that explores label dependencies and semantics by using graph convolutional networks (GCN). Particularly, we introduce an efficient correlation matrix to model label correlation based on occurrence and co-occurrence probabilities. To enrich the semantic information of labels, we design a method to use external information from Wikipedia for label embeddings. Correlated label information learned from GCN is combined with fine-grained document representation generated from another sub-net for classification. Experimental results on three benchmark datasets show that our model outweighs prior state-of-the-art methods. Ablation studies also show several aspects of the proposed model. Our code is available at https://github.com/chiennv2000/LR-GCN.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Graph convolutional network; Multi-label classification; Correlation matrix; Label embedding; Label correlation

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