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《Journal of Nantong University(Natural Science Edition)》 2013-01
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Parallel Topic Modeling Techniques Based on Fast Belief Propagation

GAO En-ting1,GU Yi-qing2,YAN Jian-feng2(1.School of Electronics and Information Engineering,Suzhou University of Science and Technology,Suzhou 215011,China;2.School of Computer Science and Technology,Suzhou University,Suzhou 215006,China)  
Fast probabilistic topic modeling such as Latent Dirichlet Allocation(LDA) is widely employed in many fields including documents topic detection,automatic documents abstracting.To learn the parameters of LDA model,a parallel Belief Propagation(BP) algorithm is designed and implemented.Running on a multi-core server in a shared-memory way,the algorithm can immediately be used to infer LDA parameters to find the relationship between different topics and words within the documents.Experimental results on Enron and Wikipedia datasets confirm that the proposed fast BP algorithm can efficiently process data on a large scale and achieve a much better accuracy than the traditional Gibbs Sampling(GS) algorithm in terms of perplexity.
【Fund】: 国家自然科学基金项目(61003259 61272449)
【CateGory Index】: TP181
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