A Comparison of Several AI Techniques for Authorship Attribution on Romanian Texts

Sanda-Maria Avram () and Mihai Oltean
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Sanda-Maria Avram: Faculty of Mathematics and Computer Science, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
Mihai Oltean: Independent Researcher, 515600 Cugir, Romania

Mathematics, 2022, vol. 10, issue 23, 1-35

Abstract: Determining the author of a text is a difficult task. Here, we compare multiple Artificial Intelligence techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are artificial neural networks, multi-expression programming, k-nearest neighbour, support vector machines, and decision trees with C5.0. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate acceptable error rates on the test set.

Keywords: authorship attribution; artificial neural networks; multi-expression programming; k-nearest neighbour; support vector machines; decision trees (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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