A Novel Query Extension Method Based on LDA.

Information retrieval (IR) is a major technology helping people to retrieve the information they are interesting in. One of challenge in IR is that the input query consists of very few words so that IR can't catch the user's intention. The pseudo correlation query extension (PCQE) is a power technology in IR aim to solve this challenge. In this paper, we propose a PCQE method which based on LDA, it apply the LDA model to fit the document set, then the latent topics are exploited and each document is represented as a multinomial distribution over topics. We calculate the probability of the document generating the query to measure the correlation between them, then the documents are ranked in terms of the correlation and top documents are extracted to seek informative words to extend the original query. Our experiment on the Ohsumed data set shows our method outperforms the other state-of-art PCQE methods.