Selecting Discriminative Terms for Relevance Model.

Pseudo-relevance feedback based on the relevance model does not take into account the inverse document frequency of candidate terms when selecting expansion terms. As a result, common terms are often included in the expanded query constructed by this model. We propose three possible extensions of the relevance model that address this drawback. Our proposed extensions are simple to compute and are independent of the base retrieval model. Experiments on several TREC news and web collections show that the proposed modifications yield significantly better MAP, precision, NDCG, and recall values than the original relevance model as well as its two recently proposed state-of-the-art variants.