OLDIES BUT GOLDIES: THE POTENTIAL OF CHARACTER N-GRAMS FOR ROMANIAN TEXTS
DOI:
https://doi.org/10.24193/subbi.2025.02Keywords:
authorship attribution, machine learning, character N-gramAbstract
This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques — Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect classification in four out of fifteen runs when using 5-gram features. These results demonstrate that lightweight, interpretable character n-gram approaches can deliver state-of-the-art accuracy for Romanian authorship attribution, rivaling more complex methods. Our findings highlight the potential of simple stylometric features in resource-constrained or under-studied language settings.
2010 Mathematics Subject Classification. 68T50.
1998 CR Categories and Descriptors. I.2.7 [Artificial Intelligence]: Natural Language Processing – Text Analysis; I.2.6 [Artificial Intelligence]: Learning – Induction.
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