## 2 Literature Review
Specifically, [PERSON] et al. [23] develop a keyword search algorithm for a corpus of Kazakh texts, incorporating the language's morphological characteristics and utilizing neural networks to enhance search accuracy. [PERSON] et al. propose a hybrid approach to semantic text analysis in the Kazakh language, combining statistical and linguistic methods [5]. This allows consideration of the agglutinative nature of Kazakh and improves the accuracy of text analysis. [PERSON] and [PERSON] [24] examine various segmentation methods to improve language modeling and text prediction for low-resource and agglutinative languages, including Kazakh. However, from the perspective of applied linguistics, developing hybrid methods for near-duplicate detection that account for the specific features of the Kazakh language remains an important and promising direction. It is important to recognize that, in addition to text, documents can contain a variety of data types, including images, mathematical formulas, numerical data organized into tables, and more. The detection of multimedia borrowings, such as images and diagrams, requires specialized methods. Techniques such as keypoint-based algorithms (SIFT, SURF) and perceptual hashing enable the identification of modified visuals [25; 26; 27]. Deep learning models, including CNNs and RNNs, further enhance plagiarism detection by analyzing complex textual and graphical data [28]. Hybrid methods that integrate TF-IDF with semantic understanding, such as the TSWT structure and HowNet database, outperform traditional algorithms in text similarity tasks, improving precision and recall [29]. Advanced anti-plagiarism systems also assess citation quality and originality levels, guiding researchers and students to produce higher-quality, original work [30]. It is particularly interesting to explore the performance of hybrid methods for near-duplicate identification in agglutinative and low-resource languages. Based on a review of well-known methods, a comparative analysis was conducted, and the main advantages and disadvantages of the near-duplicate detection methods were identified; they are presented in Table 1.  
Kazakh is an agglutinative Turkic language where grammatical meanings are expressed via sequences of suffixes. This leads to a large number of word forms. By some estimates, there are over 750 affixes [31], which complicates morphological analysis and hinders direct comparison of texts. Semantically equivalent words may appear in different surface forms, which can reduce the accuracy of lexical matching. Kazakh has a relatively free word order (default SOV) thanks to rich case morphology, which marks syntactic roles [32]. This creates challenges for methods based on word order, as similar texts may differ in word sequence. Additionally, Kazakh lacks grammatical gender and articles; definiteness is marked contextually or via case endings. Verbs are inflected for tense, mood, person, and number, often eliminating the need for personal pronouns. In contrast, English relies on a fixed word order and auxiliary words (articles, prepositions, etc.) [33]. Lexically, Kazakh exhibits high synonymy and polysemy, featuring numerous loanwords alongside native terms. As a result, semantic analysis requires vector-based models that can recognize meaning equivalence across different word choices. Overall, effective comparison of Kazakh texts requires a hybrid approach: morphological normalization, syntactic parsing, and semantic models resilient to paraphrasing and lexical variability.