Text Retrieval analysis based on Deep Learning

DOI

10.2991/isci-15.2015.176How to use a DOI?

Keywords

Text Retrieval; Replicate Softmax Models; Deep Learning; Deep Boltzmann Machines

Abstract

In view of the advantages of deep learning model in the extraction of abstract concept, a new text clustering algorithm is designed based on Deep Boltzmann Machines. Based on Replicate Softmax Model and new Deep Boltzmann Machine, energy function of this model is proposed and the detail learning algorithm is introduced. The learning can be made more efficient by using a layer-by-layer “pre-training” phase that allows variation inference to be initialized with a single bottom up pass. The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than low learning. The 20-newsgroups document sets experiment results illustrated that the novel algorithm learn good generative models, get the better competence of a shallow model- Replicate Softmax Model in handling with an extract abstract concept and has good feasibility in large scale text clustering analysis.

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/).

Download article (PDF)

TY  - CONF
AU  - Kai Liu
AU  - Limin Zhang
AU  - Yongwei Sun
PY  - 2015/01
DA  - 2015/01
TI  - Text Retrieval analysis based on Deep Learning
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 1328
EP  - 1331
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.176
DO  - 10.2991/isci-15.2015.176
ID  - Liu2015/01
ER  -