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Authorship attribution in portuguese using character N-grams
Orientador(es)
Resumo(s)
For 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.
Descrição
Palavras-chave
Language Authorship attribution Character n-grams Portuguese Stylometry Computational linguistics Machine learning
Contexto Educativo
Citação
Editora
Budapest University of Technology and Economics