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Computational Intelligence and Neuroscience
Volume 2015, Article ID 650527, 9 pages
Research Article

Learning Document Semantic Representation with Hybrid Deep Belief Network

1Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Key Laboratory of Computational Linguistics, Peking University, Ministry of Education, Beijing 100871, China
3Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received 26 September 2014; Revised 2 March 2015; Accepted 9 March 2015

Academic Editor: Pasi A. Karjalainen

Copyright © 2015 Yan Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


High-level abstraction, for example, semantic representation, is vital for document classification and retrieval. However, how to learn document semantic representation is still a topic open for discussion in information retrieval and natural language processing. In this paper, we propose a new Hybrid Deep Belief Network (HDBN) which uses Deep Boltzmann Machine (DBM) on the lower layers together with Deep Belief Network (DBN) on the upper layers. The advantage of DBM is that it employs undirected connection when training weight parameters which can be used to sample the states of nodes on each layer more successfully and it is also an effective way to remove noise from the different document representation type; the DBN can enhance extract abstract of the document in depth, making the model learn sufficient semantic representation. At the same time, we explore different input strategies for semantic distributed representation. Experimental results show that our model using the word embedding instead of single word has better performance.