Query expansion is a well known method to improve the performance of information
retrieval systems. In this work we have tested different approaches to extract
the candidate query terms from the top ranked documents returned by the first-pass
retrieval. One of them is the cooccurrence approach, based on measures of
cooccurrence of the candidate and the query terms in the retrieved documents.
The other one, the probabilistic approach, is based on the probability distribution
of terms in the collection and in the top ranked set. We compare the retrieval
improvement achieved by expanding the query with terms obtained with different
methods belonging to both approaches. Besides, we have developed a naive
combination of both kinds of method, with which we have obtained results that
improve those obtained with any of them separately. This result confirms that
the information provided by each approach is of a different nature and, therefore,
can be used in a combined manner.