Most information retrieval systems rely on the strict equality of terms between document and query in order to retrieve relevant documents to a given query. The term mismatch problem appears when users and documents' authors use different terms to express the same meaning. Statistical translation models are proposed as an effective way to adapt language models in order to mitigate term mismatch problem by exploiting semantic relations between terms. However, translation probability estimation is shown as a crucial and a hard practice within statistical translation models. Therefore, we present an alternative approach to statistical translation models that formally incorporates semantic relations between indexing terms into language models. Experiments on different CLEF corpora from the medical domain show a statistically significant improvement over the ordinary language models, and mostly better than translation models in retrieval performance. The improvement is related to the rate of general terms and their distribution inside the queries.
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