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	Learning document semantic representation with hybrid deep belief network
	Yan Yan ; Yin Xu-Cheng ; Li Sujian ; Yang Mingyuan ; Hao Hong-Wei
	2015
DOI	10.1155/2015/650527
英文摘要	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.; SCI(E); EI; PubMed; 0; ARTICLE; xuchengyin@ustb.edu.cn; 650527; 2015
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/188084]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yan Yan,Yin Xu-Cheng,Li Sujian,et al. Learning document semantic representation with hybrid deep belief network[J],2015.
APA	Yan Yan,Yin Xu-Cheng,Li Sujian,Yang Mingyuan,&Hao Hong-Wei.(2015).Learning document semantic representation with hybrid deep belief network..
MLA	Yan Yan,et al."Learning document semantic representation with hybrid deep belief network".(2015).

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