  Automatic text categorization is a complex and useful task for many natural
language processing applications. Recent approaches to text categorization
focus more on algorithms than on resources involved in this operation. In
contrast to this trend, we present an approach based on the integration of
widely available resources as lexical databases and training collections to
overcome current limitations of the task. Our approach makes use of WordNet
synonymy information to increase evidence for bad trained categories. When
testing a direct categorization, a WordNet based one, a training algorithm, and
our integrated approach, the latter exhibits a better perfomance than any of
the others. Incidentally, WordNet based approach perfomance is comparable with
the training approach one.
