Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification
Joint Authors
Liu, Li
Wang, Tianshi
Zhang, Huaxiang
Zhang, Long
Chen, Xiuxiu
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-30
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
With the continuous renewal of text classification rules, text classifiers need more powerful generalization ability to process the datasets with new text categories or small training samples.
In this paper, we propose a text classification framework under insufficient training sample conditions.
In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively.
Then, we use the real textual features to train a generator and a discriminator so as to make the distribution of generated data consistent with that of real data.
Finally, the classifier is cooperatively trained by real data and generated data.
Extensive experimental validation on four public datasets demonstrates that our method significantly performs better than the comparative methods.
American Psychological Association (APA)
Wang, Tianshi& Liu, Li& Zhang, Huaxiang& Zhang, Long& Chen, Xiuxiu. 2020. Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification. Complexity،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1144370
Modern Language Association (MLA)
Wang, Tianshi…[et al.]. Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification. Complexity No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1144370
American Medical Association (AMA)
Wang, Tianshi& Liu, Li& Zhang, Huaxiang& Zhang, Long& Chen, Xiuxiu. Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification. Complexity. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1144370
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144370