## 3 Materials and Methods  
The methodology of this study focuses on the development and testing of combined models for text data analysis aimed at detecting near duplicates in academic and scientific documents written in Kazakh language. The primary emphasis is on creating a hybrid system that integrates several approaches, including N-gram analysis, locality-sensitive hashing (LSH), LSA, and probabilistic topic models. The first stage of the research involved collecting data from various sources, including scientific articles, theses, term papers, and dissertations, all of which are available in open-access databases in the Kazakh language. The dataset included both original documents and works with partially modified sections. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Kazakh Language** & **English Language** \\ \hline Language type & Agglutinative & Analytic \\ Morphology & Rich suffix-based inflection & Limited inflectional \\ Word order & Flexible (SOV) & Fixed (SVO) \\ Articles and gender & No articles or grammatical & Articles present, gender in \\  & gender & pronouns \\ Verb system & Complex affixation & Analytic tense/aspect \\ Lexical features & High synonymy and & More standardized \\  & polysemy & vocabulary \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The features of the Kazakh language in comparison with English. The combined model developed for text analysis incorporates several key components. N-gram analysis was used to identify matches at the phrase level and detect paraphrased text fragments, with bigrams and trigrams tested for optimal accuracy. LSH was employed for the rapid detection of similar text fragments, allowing the identification of partially modified sections and significantly speeding up data processing. LSA was applied for the semantic comparison of texts, identifying conceptual similarity between fragments, which is useful for detecting hidden plagiarism. The probabilistic topic model (PTM) facilitated the analysis of the thematic structure of the text and the evaluation of the completeness of topic representation in publications, which is particularly important for assessing the thematic alignment of scientific publications. The model was trained on the collected data, including both original and borrowed texts. Although the term cross-validation is used here in a broad sense, we did not apply a classical k-fold cross-validation procedure, since LSA and LDA do not require iterative optimization via gradient descent. Instead, the corpus was randomly split into an 80% training set and a 20% test set. From the training set, an additional 20% hold-out subset was used for tuning threshold hyperparameters. The final evaluation of model performance (precision, recall, F1-score) was conducted on the independent test set. Performance evaluation was conducted using precision, recall, and F1-score metrics, enabling an objective assessment of the model's effectiveness in detecting near duplicates. Various parameters were tested for each method, including the N-gram size and the number of topics, to optimize model settings and improve accuracy. Model validation included comparisons with N-gram, TF-IDF, analysis of false positives, and assessment of processing time, which ultimately refined and enhanced the quality of the combined model's performance. The results of testing the models on prepared datasets enabled a comparative analysis of their performance and revealed the specific features of each method when applied to Kazakh language texts. The proposed approach ensures the adaptation of existing methods to the linguistic characteristics of the language, making them more suitable for text similarity analysis tasks. It is known that documents can contain data of various types: text, images, tables, mathematical formulas, diagrams, and charts. For each of these data types, specialized analysis methods need to be applied. Image analysis in documents can be outperformed using both standard software applications and additional custom modules.