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Evolving Co-occurrence Based Query Expansion Schemes in Information Retrieval Using Genetic Programming

Global query expansion techniques have long been proposed as a solution to overcome the problem of term mismatch between a query and its relevant documents. This paper describes a method which automatically tackles the problems of how to find the best terms for the expansion of a particular query and secondly, how to weight these terms for use with the original query. Genetic Programming is used to evolve schemes for term selection using global (collection-wide) co-occurrence measures. The schemes evolved are also used to weight the term in the expanded query as they are a measure of the term’s importance in relation to the query. As a result, the genetic program has to learn a suitable scheme for identifying the best correlates for the query concept and also a scheme that correctly weights these in relation to each other. These schemes are tested on standard test collections and show a significant increase in performance on the training data but only modest improvement on the collections that are not included in training.