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

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Time Added
2022/12/12 19:34
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Authors
Sanda-Maria Avram and Mihai Oltean Sanda-Maria Avram: Faculty of Mathematics and Computer Science BabeČ™-Bolyai University 400084 Cluj-Napoca Romania Mihai Oltean: Independent Researcher 515600 Cugir Romania
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)
Year Published
2022
Series
Mathematics 2022 vol. 10 issue 23 1-36
Rank
0.53
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