Improving Query Expansion Using WordNet

Abstract

This study proposes a new way of using WordNet for Query Expansion (QE). We choose candidate expansion terms, as usual, from a set of pseudo relevant documents; however, the usefulness of these terms is measured based on their definitions provided in a hand-crafted lexical resource like WordNet. Experiments with a number of standard TREC collections show that this method outperforms existing WordNet based methods. It also compares favorably with established QE methods such as KLD and RM3. Leveraging earlier work in which a combination of QE methods was found to outperform each individual method (as well as other well-known QE methods), we next propose a combination-based QE method that takes into account three different aspects of a candidate expansion term's usefulness: (i) its distribution in the pseudo relevant documents and in the target corpus, (ii) its statistical association with query terms, and (iii) its semantic relation with the query, as determined by the overlap between the WordNet definitions of the term and query terms. This combination of diverse sources of information appears to work well on a number of test collections, viz., TREC123, TREC5, TREC678, TREC robust new and TREC910 collections, and yields significant improvements over competing methods on most of these collections.