## 5 Results  
We conducted an experiment aimed at comparative evaluation of four approaches to detecting close duplicates in Kazakh texts: N-gram analysis, the TF-IDF method, the base BERT model, and the proposed hybrid model combining semantic and syntactic analysis. The following key metrics were used to comprehensively evaluate the performance of each method: precision (Precision), completeness (Recall), F1-measure, and processing time. The dataset Arailym-aitu/KazakhTextDuplicates, formed based on Arailym-theubayeva/small_kazakh_corpus [34], includes 25,922 text fragments covering a wide range of topics and style variants, which ensures its representativeness for modeling real scenarios of partial borrowing. The analysis shows that the system of controlled modifications, including word rearrangements, replacement of key terms with synonyms and morphological transformations, allows the efficient generation of pairs of texts classified into exact, partial, paraphrase and contextual types, which reflects the diversity of ways of processing the source material. The normalization of texts by lower-casing, removal of redundant characters, and stop word filtering helps to reduce the impact of minor variations, which improves the accuracy of the markup. The obtained dataset demonstrates high quality of markup and homogeneity of data, which makes it a valuable resource for the development and testing of plagiarism detection algorithms and the adaptation of existing methods to the peculiarities of the agglutinative and low-resource Kazakh language. Standard statistical metrics, such as precision, recall, F1-score, and processing time are used to quantify the performance of algorithms for detecting textual duplicates in the KazakhTextDuplicates dataset. Precision is defined as the proportion of correctly identified duplicates in relation to the total number of detected duplicates, which allows us to assess the propensity of the model to produce false positives; recall measures the proportion of correctly found duplicates in relation to the total number of true borrowings, demonstrating the ability of the algorithm to detect all relevant cases; F1-score, being a harmonic average of precision and recall, serves to assess the balance of the model; processing time characterizes the computational efficiency and applicability of the methods when working with large datasets The complex analysis of these indicators allows us to objectively compare the performance of different approaches (such as N-gram, TF-IDF, and the hybrid model) and optimize their parameters in order to improve the accuracy and reliability of automatic plagiarism detection systems in low-resource Kazakh. At the preprocessing stage, all the texts were lower-cased and punctuation marks and stop words were removed to reduce noise and ensure a uniform presentation of the data. The results of the N-gram analysis depended significantly on the chosen value of n. Unigrams achieved perfect accuracy (1.00) and completeness (1.00), but the lack of context consideration limited the detection of paraphrased or partially modified texts. When mov \begin{table}
\begin{tabular}{c c} \hline \hline
**Type\_Duplicate** & **Count** \\ \hline exact & 6597 \\ partial & 6468 \\ paraphrase & 6439 \\ contextual & 6418 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset characteristics. ing to bigrams and trigrams, there is a decrease in completeness to 0.51 while maintaining accuracy at 1.00, resulting in an F1-measure of 0.67. At the same time processing time increases from 14.81 s (bigrams) to 16.79 s (trigrams) (see Table 4 and Figure 2). Thus, N-gram analysis effectively detects exact matches, but fails with paraphrased or partially modified texts. The TF-IDF model showed an F1-measure of 0.67, which is comparable to the N-gram approach, but with a different balancing of metrics: accuracy was 0.51 and completeness was 1.00. The high completeness indicates the model's ability to detect most modified texts, but the reduced accuracy indicates a higher number of false positives.