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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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 31; H. 1; S. 295 - 308
Hauptverfasser:	Xu, Weidi, Tan, Ying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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