Stylometric Analysis in the Task of Searching for Borrowings of Texts in the Tatar Language
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Abstract
This article discusses the use of stylometric analysis in searching for borrowings of text in the Tatar language. Relevant tools have been developed, utilizing machine learn-ing algorithms, including clustering (k-means method), classification (random forest method, support vector machine method, naive Bayes classifier), and a hybrid approach (FastText model + logistic regression). Special attention is paid to the adaptation of lin-guistic metrics for the Tatar language.
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