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Aiming al the retrieval semantic information in Mongolian, this paper proposed a new method combined topic model latent dirichlet allocation (LDA) and language model. This method modeled Mongolian documents with LDA and language model, estimated parameters with Gibbs sampling and represented probability of word, it could mine the hidden relationship between the different topics and the words from documents, got the topic distribution and computed the similarity of keywords topics. Finally, it returned to the most relevant documents with topics. Experimental results show that the method has a higher performance in topic semantic compared with one sole model. [ABSTRACT FROM AUTHOR]

Traditional topic model assumes that each document is related only one topic, but in practice, a document is usually related with multi topics. This paper represented a document with combination of multi topics with the LDA model, and proposed a LDA based mixture model for Ad hoc information retrieval method in language model framework. The proposed method combined the LDA and the document model. Compared with cluster-based model, it has a higher performance, while keeping low computation complexity, and thus can be used in information retrieval of large scale document collections. [ABSTRACT FROM AUTHOR]