A Novel Hybrid Correlation Measure for Query Expansion-Based Information Retrieval

Abstract

Query expansion (QE) is one of the most effective techniques to enhance the retrieval performance and to retrieve more relevant information. It attempts to build more useful queries by enriching the original queries with additional expansion terms that best characterize the users' information needs. In this chapter, the authors propose a new correlation measure for query expansion to evaluate the degree of similarity between the expansion term candidates and the original query terms. The proposed correlation measure is a hybrid of two correlation measures. The first one is considered as an external correlation and it is based on the term co-occurrence, and the second one is considered as an internal correlation and it is based on the term proximity. Extensive experiments have been performed on MEDLINE, a real dataset from a large online medical database. The results show the effectiveness of the proposed approach compared to prior state-of-the-art approaches.