WORD AND PUNCTUATION N-GRAM FEATURES IN ROMANIAN AUTHORSHIP ATTRIBUTION
DOI:
https://doi.org/10.24193/subbi.2025.04Keywords:
authorship attribution, machine learning, N-gram featuresAbstract
This study addresses the problem of authorship attribution for Romanian texts, focusing on the use of N-gram features with an emphasis on semantic-independent representations. While character N-grams have been previously studied, this work extends the exploration to word and part-of-speech (POS) N-grams, as well as combinations involving punctuation, closed-class words, and filtered content words. Using the ROST corpus, we evaluate six supervised learning algorithms, with results aver- aged over multiple runs to ensure robustness. Our experiments show that Artificial Neural Networks (ANN) consistently achieve the highest performance, with word-based unigrams enhanced by punctuation reaching an average macro-accuracy of 0.93. Importantly, semantically independent features, such as closed-class words and POS replacements for nouns and verbs, yield small further improvements. These findings highlight the effectiveness of carefully designed N-gram features for Romanian AA and suggest that semantic-independent representations can complement traditional lexical approaches.
2010 Mathematics Subject Classification. 68T50.
1998 CR Categories and Descriptors. code [Artificial Intelligence]: Natural Language Processing – Text Analysis; code [Artificial Intelligence]: Learning – Induction.
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