Markov, IliaBaptista, JorgePichardo-Lagunas, Obdulia2018-12-072018-12-0720171785-8860http://hdl.handle.net/10400.1/11987For the Authorship Attribution (AA) task, character n-grams are considered among the best predictive features. In the English language, it has also been shown that some types of character n-grams perform better than others. This paper tackles the AA task in Portuguese by examining the performance of different types of character n-grams, and various combinations of them. The paper also experiments with different feature representations and machine-learning algorithms. Moreover, the paper demonstrates that the performance of the character n-gram approach can be improved by fine-tuning the feature set and by appropriately selecting the length and type of character n-grams. This relatively simple and language-independent approach to the AA task outperforms both a bag-of-words baseline and other approaches, using the same corpus.engLanguageAuthorship attributionCharacter n-gramsPortugueseStylometryComputational linguisticsMachine learningjournal article10.12700/APH.14.3.2017.3.4