A Transformer-Based Framework and Preliminary Baseline Experiment for Uzbek Text Classification in Low-Resource NLP

Natural Language Processing is an important area of artificial intelligence, but many low-resource languages still lack sufficient datasets and optimized models. This paper presents a framework and preliminary baseline experiment for Uzbek text classification. The study focuses on text preprocessing, feature extraction, model selection, and evaluation. Two baseline models, TF-IDF with Logistic Regression and TF-IDF with Support Vector Machine, are used for comparison. The models are evaluated using accuracy, precision, recall, and F1-score. The proposed framework can support future Uzbek NLP applications in education, media, document classification, and automated text processing.

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