Learning Document Semantic Representation with Hybrid Deep Belief Network
Yan, Yan; Yin, Xu-Cheng; Li, Sujian; Yang, Mingyuan; Hao, Hong-Wei
发表期刊COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
2015
文章类型Article
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.
WOS标题词Science & Technology ; Life Sciences & Biomedicine
收录类别SCI
语种英语
WOS研究方向Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目Mathematical & Computational Biology ; Neurosciences
WOS记录号WOS:000352360600001
引用统计
被引频次:14[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/40862
专题复杂系统认知与决策实验室_听觉模型与认知计算
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GB/T 7714
Yan, Yan,Yin, Xu-Cheng,Li, Sujian,et al. Learning Document Semantic Representation with Hybrid Deep Belief Network[J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE,2015.
APA Yan, Yan,Yin, Xu-Cheng,Li, Sujian,Yang, Mingyuan,&Hao, Hong-Wei.(2015).Learning Document Semantic Representation with Hybrid Deep Belief Network.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE.
MLA Yan, Yan,et al."Learning Document Semantic Representation with Hybrid Deep Belief Network".COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2015).
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