Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). 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 non-lemmatized selection of verbs and nouns, adjectives and adverbs 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.
|Idioma original||Inglés estadounidense|
|Número de páginas||6|
|Estado||Publicada - 1 ene 2016|
|Publicado de forma externa||Sí|
|Evento||AAAI Workshop - Technical Report - |
Duración: 1 ene 2016 → …
|Conferencia||AAAI Workshop - Technical Report|
|Período||1/01/16 → …|
Bonatti, R., De Paula, A. G., Lamarca, V. S., & Cozman, F. G. (2016). Effect of part-of-speech and lemmatization filtering in email classification for automatic reply. 496-501. Papel presentado en AAAI Workshop - Technical Report, .