Semisupervised Text Classification by Variational Autoencoder

Semisupervised text classification has attracted much attention from the research community. In this paper, a novel model, the semisupervised sequential variational autoencoder (SSVAE), is proposed to tackle this problem. By treating the categorical label of unlabeled data as a discrete latent varia...

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Bibliographic Details

Published in:	IEEE transaction on neural networks and learning systems Vol. 31; no. 1; pp. 295 - 308
Main Authors:	Xu, Weidi, Tan, Ying
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Classification Data models Decoders Decoding Feature extraction Generative models Lower bounds Mathematical analysis Performance enhancement Predictive models Semisupervised learning Task analysis text classification Training variational autoencoder (VAE)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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