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arXiv · 2507.12064

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features

Jeremi K. Ochab·Mateusz Matias·Tymoteusz Boba·Tomasz Walkowiak
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Abstract

This submission to the binary AI detection task is based on a modular stylometric pipeline, where: public spaCy models are used for text preprocessing (including tokenisation, named entity recognition, dependency parsing, part-of-speech tagging, and morphology annotation) and extracting several thousand features (frequencies of n-grams of the above linguistic annotations); light-gradient boosting machines are used as the classifier. We collect a large corpus of more than 500 000 machine-generated texts for the classifier's training. We explore several parameter options to increase the classifier's capacity and take advantage of that training set. Our approach follows the non-neural, computationally inexpensive but explainable approach found effective previously.

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Jeremi K. Ochab, Mateusz Matias, Tymoteusz Boba, Tomasz Walkowiak. 2025-07-16. StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features. https://arxiv.org/abs/2507.12064

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