Improving document retrieval according to prediction of query difficulty

Our experiments in the Robust track this year focused on predicting query difficulty and using this prediction for improving information retrieval. We developed two prediction algorithms and used the subsequent prediction in several ways in order to improve the performance of the search engine. These included modifying the search engine parameters, using selective query expansion, and switching between different query parts. We also experimented with a new scoring model based on ideas from the field of machine learning. Our results show that query prediction is indeed efficient in improving retrieval, although further work is needed in order to improve the performance of the prediction algorithms and their uses.