Training a classifier for the selection of good query expansion terms with a genetic algorithm.
Lourdes Araujo, Joaquín Pérez-Iglesias

Proc. IEEE Congress on Evolutionary Computation (CEC 2010)
IEEE Press, pp 1-8 (2010)

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>