Query anchoring using discriminative query models

Pseudo-feedback-based query models are induced from a result list of the documents most highly ranked by initial search performed for the query. Since the result list often contains much non-relevant information, query models are anchored to the query using various techniques. We present a novel unsupervised discriminative query model that can be used, by several methods proposed herein, for query anchoring of existing query models. The model is induced from the result list using a learning-to-rank approach, and constitutes a discriminative term-based representation of the initial ranking. We show that applying our methods to generative query models can improve retrieval performance.