Talk · International Siberian workshops and tutorials on electron devices and materials · June 1, 2026

Classification of Uzbek Banking Texts by Document Types

Nilufar Z. Abdurakhmonova, Sadokat Nurmanova, Mushtariy Kozimjonova, Salomat Otamurodova, Toshpulatova Mokhinur, Alisher Abidjanov

Nilufar Abdurakhmonova

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Abstract

The article describes a method for automating text classification by document types using logistic regression and N-gram-based text vectorization. It should be noted that the current study focuses on developing an algorithm that can process both simple and complex text data. Including texts that have different thematic contexts. Moreover, for the most objective assessment of the developed tool, three data sets were prepared, each with its own degree of complexity. As a result of testing, it was found that the algorithm showed fairly high accuracy on texts with a clear structure, in particular, the best value of metrics was achieved when using early stopping, as well as hyperparameter optimization. In addition, authors organized detailed comparative analysis of existing alternative works, which can help to see current actual problems of the considered topic. In the future, the authors plan to implement the proposed approach using neural network technologies, which will significantly expand the capabilities of this algorithm.

Text and Document Classification TechnologiesTopic ModelingComputational and Text Analysis Methodsdocument type classificationtext classificationlogistic regression

Classification of Uzbek Banking Texts by Document Types

Abstract

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