  Automatic email categorization is an important application of text
classification. We study the automatic reply of email business messages in
Brazilian Portuguese. We present a novel corpus containing messages from a real
application, and baseline categorization experiments using Naive Bayes and
support Vector Machines. We then discuss the effect of lemmatization and the
role of part-of-speech tagging filtering on precision and recall. Support
Vector Machines classification coupled with nonlemmatized selection of verbs,
nouns and adjectives was the best approach, with 87.3% maximum accuracy.
Straightforward lemmatization in Portuguese led to the lowest classification
results in the group, with 85.3% and 81.7% precision in SVM and Naive Bayes
respectively. Thus, while lemmatization reduced precision and recall,
part-of-speech filtering improved overall results.
