Training a classifier for the selection of good query expansion terms with a genetic algorithm

Retrieving precise information from large collections of documents or from the web is an important task in our world. The specification of the information needed is done in form of a sequence of terms or query, which is frequently too short or unspecific to allow selecting a set of relevant documents small enough to be inspected by the user. This problem can be alleviated by expanding the query with other terms that make it more specific. The selection of these possible expansion terms is the problem addressed in this work. We have developed a classifier which has been trained for distinguishing good expansion terms. The identification of good terms to train the classifier has been achieved with a genetic algorithm whose fitness function is based on users´ relevance judgements on a set of documents. Results show that the training performed by the genetic algorithm is able to improve the quality of the query expansion results.