## 1 Introduction  
In recent years, there has been a rapid increase in the volume of digital content, which demands increasingly accurate and efficient methods for originality verification. As of 2023, approximately 30% of academic articles and student papers in universities worldwide contain instances of borrowing, with 10% of these cases resulting from deliberate plagiarism [1]. In the context of global digitalization, the task of detecting not only direct copies but also near duplicates has become more relevant than ever. It is expected that by 2025, the global market for plagiarism detection software will exceed USD 2.5 billion, with combined data analysis models used for detecting near duplicates constituting a significant share. To protect intellectual property and maintain academic integrity, increasing attention is being paid to methods for detecting duplicate and similar texts. However, in certain languages, particularly agglutinative ones like Kazakh, the application of standard approaches may be less effective. Agglutination leads to the formation of long morphological chains, complicating the task of identifying matches at the token level [2; 3]. This necessitates the investigation and comparison of various methods capable of accurately detecting near duplicates in the Kazakh language. Near duplicates are text fragments that partially match the original but have differences such as paraphrasing, structural changes, or minor content modifications. For instance, paraphrasing and structural modification of text are the most common plagiarism techniques, accounting for up to 60% of all cases of borrowing [4]. Detecting such near duplicates in the Kazakh language requires more complex algorithms than standard methods for finding identical fragments. This is because traditional anti-plagiarism systems, based on simple text comparisons, often prove insufficiently effective at identifying subtle changes and partial borrowing. Agglutination in the Kazakh language complicates text processing, as a single word can contain numerous suffixes and affixes that alter its lexical and grammatical function. Methods focused solely on matching individual lexemes may overlook semantic similarity. Hybrid approaches that account not only for statistical metrics (e.g., TF-IDF) but also for semantic components (e.g., latent semantic analysis (LSA)) enable a more accurate consideration of the diversity of morphological forms in the Kazakh language. Based on existing research, it can be stated that text data analysis, particularly for agglutinative languages, requires not only the application of standard methods but also their adaptation to the language's specific features. Previous studies demonstrate that traditional approaches, such as statistical methods or frequency-based analysis, can be useful; however, they often face limitations when processing texts with high morphological complexity. This highlights the need for more flexible solutions that consider both surface-level and deep-level textual similarity [5]. The main features of modern text data analysis systems that contribute to their popularity include accuracy and adaptability to various types of data. The use of advanced analysis methods, such as locality-sensitive hashing and N-gram analysis, enables the detection of text fragments with a high degree of similarity, even in the presence of significant text modifications. Studies show that combined models, which integrate statistical and semantic approaches can improve the accuracy of detecting near duplicates by 25% compared to traditional algorithms [6]. These algorithms include text preprocessing and indexing, which not only enhance accuracy but also significantly reduce analysis time--from several hours to minutes for a standard academic document. These anti-plagiarism systems play an important role not only in maintaining academic integrity but also in human resource management, as they help create transparent and objective conditions for evaluating the knowledge and skills of students and applicants.