This paper proposes a novel representation for Authorship Attribution (AA), based on Concise Semantic Analysis (CSA), which has been successfully used in Text Categorization (TC). Our approach for AA, called Document Author Representation (DAR), builds document vectors in a space of authors, calculating the relationship between textual features and authors. In order to evaluate our approach, we compare the proposed representation with conventional approaches and previous works using the c50 corpus. We found that DAR can be very useful in AA tasks, because it provides good performance on imbalanced data, getting comparable or better accuracy results. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
López-Monroy, A. P., Montes-Y-Gómez, M., Villaseñor-Pineda, L., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2012). A new document author representation for authorship attribution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7329 LNCS, pp. 283–292). https://doi.org/10.1007/978-3-642-31149-9_29
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