Analyzing the effect of query class on document retrieval performance

Abstract

Analysis of queries posed to open-domain question-answering systems indicates that particular types of queries are dominant, e.g., queries about the identity of people, and about the location or time of events. We applied a rule-based mechanism and performed manual classification to classify queries into such commonly occurring types. We then experimented with different adjustments to our basic document retrieval process for each query type. The application of the best retrieval adjustment for each query type yielded improvements in retrieval performance. Finally, we applied a machine learning technique to automatically learn the manually classified query types, and applied the best retrieval adjustments obtained for the manual classification to the automatically learned query classes. The learning algorithm exhibited high accuracy, and the retrieval performance obtained for the learned classes was consistent with the performance obtained for the rule-based and manual classifications. © Springer-Verlag Berlin Heidelberg 2004.