Support Vector Machines have been applied to text classification with great success. In this paper, we apply and evaluate the impact of using part-of-speech tags (nouns, proper nouns, adjectives and verbs) as a feature selection procedure in a European Portuguese written dataset - the Portuguese Attorney General's Office documents. From the results, we can conclude that verbs alone don't have enough information to produce good learners. On the other hand, we obtain learners with equivalent performance and a reduced number of features (at least half) if we use specific part-of-speech tags instead of all words.
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