A text classification model constructed by Latent Dirichlet Allocation and Deep Learning

DOI

10.2991/icmmcce-15.2015.482How to use a DOI?

Keywords

text classification, latent Dirichlet allocation, deep learning, Gibbs sampling

Abstract

In this paper, we proposed a mixed model of text classification constructed by latent dirichlet allocation and deep learning. The model present that a text will be represent as a vector computing by latent dirichlet allocation algorithm, and this vector is probabilistic vector of corresponding topic words space. Then we input these topic vectors into a deep learning framework for computing nonlinear relationship of each vector. Finally, we constructed a text classification system. The proposed model achieves a higher accuracy when compared with other current popular algorithms, such as SVM, KNN and TFIDF.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yu Liu
AU  - Zhengping Jin
PY  - 2015/12
DA  - 2015/12
TI  - A text classification model constructed by Latent Dirichlet Allocation and Deep Learning
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.482
DO  - 10.2991/icmmcce-15.2015.482
ID  - Liu2015/12
ER  -