An approach for semantic query expansion based on maximum entropy-hidden Markov model

The ineffectiveness of information retrieval systems is mostly caused by the inaccurate query formed by a few keywords that reflect actual user information need. One well known technique to overcome this limitation is Automatic Query Expansion (AQE), whereby the user's original query is improved by adding new features with a related meaning. It has long been accepted that capturing term associations is a vital part of information retrieval. It is therefore mainly to consider whether many sources of support may be combined to forecast term relations more precisely. This is mainly significant when frustrating to predict the probability of relevance of a set of terms given a query, which may involve both lexical and semantic relations between the terms. This paper presents a approach to expand the user query using three level domain model such as conceptual level(underlying Domain knowledge), linguistic level(term vocabulary based on Wordnet), stochastic model ME-HMM2 which combines (HMM (Hidden Markov Model and Maximum Entropy(ME) models) stores the mapping between such levels, taking into account the linguistic context of words.

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