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• W2956478110 abstract "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." @default.
• W2956478110 created "2019-07-23" @default.
• W2956478110 creator A5019280723 @default.
• W2956478110 creator A5062522127 @default.
• W2956478110 date "2019-07-08" @default.
• W2956478110 modified "2023-09-27" @default.
• W2956478110 title "Development of email classifier in Brazilian Portuguese using feature selection for automatic response" @default.
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• W2956478110 hasPublicationYear "2019" @default.
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