Machine learning approaches to Uzbek-language text classification: a comparative study

This paper compares classical machine learning algorithms and transformer-based models for Uzbek-language text classification. Experiments on a corpus of 42,000 news documents show that fine-tuned multilingual models outperform TF-IDF baselines, while remaining sensitive to the morphological richness of the language.