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

• URL: http://arxiv.org/abs/2507.12064v1
• Date: Wed, 16 Jul 2025 09:21:20 GMT
• Title: StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
• Authors: Jeremi K. Ochab, Mateusz Matias, Tymoteusz Boba, Tomasz Walkowiak,
• Abstract summary: This submission to the binary AI detection task is based on a modular stylometric pipeline.<n>We collect a large corpus of more than 500 000 machine-generated texts for the classifier's training.<n>Our approach follows the non-neural, computationally inexpensive but explainable approach found effective previously.
• Score: 0.1499944454332829
• License: http://creativecommons.org/licenses/by/4.0/
• 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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