A Novel Graph Convolutional Text Classification Based on Token-Task Learning.

Graph Convolutional Network (GCN) is an effective tool for classification prediction. In a text classification task, the text is constructed as a word-document graph. However, existing methods only make document category predictions based on document nodes in the word-document graph, neglecting the auxiliary role of word nodes in classification. Based on this, this paper proposes a novel GCN structure based on Token-Task Learning (TTL) for text classification. This paper performs part of speech (POS) tagging or Named Entity Recognition (NER) as auxiliary tasks for text classification. By establishing the relationship between token classification and text classification, text category prediction can take into account the information implied by word nodes, thereby enhancing the accuracy of text classification. In addition, this paper replaces Relu in TextGCN with Mish to enhance data fitting capability of GCN. The experiments are carried out on five text classification datasets, and the experimental results show that the proposed method effectively improves the accuracy of text classification while outperforming the comparison methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)