Interactive sense feedback for difficult queries

Ambiguity of query terms is a common cause of inaccurate retrieval results. Existing work has mostly focused on studying how to improve retrieval accuracy by automatically resolving word sense ambiguity. However, fully automatic sense identification and disambiguation is a very challenging task. In this work, we propose to involve a user in the process of disambiguation through interactive sense feedback and study the potential effectiveness of this novel feedback strategy. We propose several general methods to automatically identify the major senses of query terms based on global analysis of document collection and generate concise representations of the discovered senses to the users. This feedback strategy does not rely on initial retrieval results, and thus can be especially useful for improving the results of difficult queries. We evaluated the effectiveness of the proposed methods for sense identification and presentation through simulation experiments and user studies, which both indicate that sense feedback strategy is a promising alternative to the existing interactive feedback techniques such as relevance feedback and term feedback.