title: Learning document semantic representation with hybrid deep belief network.
abstract: 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.
authors: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors: Yan
authors: Y
authors: Yan
authors: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors: Xu-Cheng
authors: XC
authors: Yin
authors: Key Laboratory of Computational Linguistics, Peking University, Ministry of Education, Beijing 100871, China.
authors: Sujian
authors: S
authors: Li
authors: Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
authors: Mingyuan
authors: M
authors: Yang
authors: Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
authors: Hong-Wei
authors: HW
authors: Hao
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D012660:Semantics