Learning conceptual descriptions of categories

In this work we propose a model to learn conceptual descriptions of categories from precategorized texts. The model is general and parametric, and it captures most of the statistical approaches to classification as well as allowing the definition of more symbolic learning schemes. The algorithm scheme has been instantiated into three different algorithms, which have been implemented and tested on a collection of documents obtained from the Web. As a possible application of the descriptions obtained, classification was done on a test set. Results are somewhat surprising, and stand in contrast with most experiments done in literature, possibly giving hints about a different research direction.