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.
View on arXiv@article{lupsa2025_2506.15650,
title={ Oldies but Goldies: The Potential of Character N-grams for Romanian Texts },
author={ Dana Lupsa and Sanda-Maria Avram and Radu Lupsa },
journal={arXiv preprint arXiv:2506.15650},
year={ 2025 }
}