Query Difficulty, Robustness, and Selective Application of Query Expansion

There is increasing interest in improving the robustness of IR systems, i.e. their effectiveness on difficult queries. A system is robust when it achieves both a high Mean Average Precision (MAP) value for the entire set of topics and a significant MAP value over its worst X topics (MAP(X)). It is a well known fact that Query Expansion (QE) increases global MAP but hurts the performance on the worst topics. A selective application of QE would thus be a natural answer to obtain a more robust retrieval system. We define two information theoretic functions which are shown to be correlated respectively with the average precision and with the increase of average precision under the application of QE. The second measure is used to selectively apply QE. This method achieves a performance similar to that with unexpanded method on the worst topics, and better performance than full QE on the whole set of topics.