Article

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

Shaxzoda YusupovaFaculty of Business IT Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent, Uzbekistan

Zenodo (CERN European Organization for Nuclear Research)repository2026en

ABI

Abstract

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.

Topics

Text and Document Classification Technologies Machine Learning and Data Classification Big Data and Digital Economy

Identifiers

DOI: 10.5281/zenodo.20241341

Citations and references

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