Development of email classifier in Brazilian Portuguese using feature selection for automatic response
This work addresses email classification for business applications in Brazilian Portuguese, but it is incremental as it applies existing methods to a new dataset with minor optimizations.
The paper tackled the problem of automatic email categorization in Brazilian Portuguese by developing a classifier using feature selection, achieving a maximum accuracy of 87.3% with Support Vector Machines and non-lemmatized selection of verbs, nouns, and adjectives.
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.