Abstract
In this paper we compare the robustness of several types of stylistic markers to help discriminate authorship at sentence level. We train a SVM-based classifier using each set of features separately and perform sentence-level authorship analysis over corpus of editorials published in a Portuguese quality newspaper. Results show that features based on POS information, punctuation and word / sentence length contribute to a more robust sentence-level authorship analysis.
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Sousa-Silva, R., Sarmento, L., Grant, T., Oliveira, E., Maia, B. (2010). Comparing Sentence-Level Features for Authorship Analysis in Portuguese. In: Pardo, T.A.S., Branco, A., Klautau, A., Vieira, R., de Lima, V.L.S. (eds) Computational Processing of the Portuguese Language. PROPOR 2010. Lecture Notes in Computer Science(), vol 6001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12320-7_7
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DOI: https://doi.org/10.1007/978-3-642-12320-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12319-1
Online ISBN: 978-3-642-12320-7
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