Study of Semantic Understanding by LDA

Abstract

Abstract: Latent Dirichlet allocation(LDA) is a popular model used in text cluster,and is proved to improve the performance of information retrieval by explaining queries and documents effectively.There are mainly two algorithms to solve the inference of LDA model:Gibbs sampling and belief propagation.This paper compared the effect of these two inference algorithms on information retrieval in different topic scales,and used two different ways to explain queries and documents.One way is representing them with document-topic distribution,the other is representing them with word refactoring.Experimental results show that document-topic distribution and Gibbs sampling inference algorithm can improve the performance of information retrieval.

Key words: Latent Dirichlet allocation,Information retrieval,Approximate inference,Textual interpretation

GAO Yang, YANG Lu, LIU Xiao-sheng and YAN Jian-feng. Study of Semantic Understanding by LDA[J].Computer Science, 2015, 42(8): 279-282.

References

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