## 6 Discussion and Conclusions  
This paper presents a hybrid method for detecting near duplicates in tables represented in documents in the Kazakh language. By using locally sensitive hashing and the nearest neighbor method, the proposed approach enables effective identification of similarities between text and numerical data, which is crucial for ensuring academic integrity and detecting potential plagiarism. Through the integration of hash methods and Hamming distance for textual content, as well as the use of metric distances for numerical data, this method can even identify partially matching data. The developed algorithm adapts to textual, numerical, and mixed data, ensuring accurate analysis of complex tables through standardization and normalization, reducing false positives. The threshold values for matching enhance precision across various data types. A comparative analysis of N-gram, TF-IDF, BERT, and the hybrid model reveals that the hybrid model outperforms the others in the terms of precision, recall, and F1-score. Its balance of statistical and semantic methods enables efficient large-scale text similarity analysis, particularly for Kazakh texts. For the task of near-duplicate detection, achieving precision = 1 is a strong result, as it indicates the complete absence of false positives. This is particularly important because if unique texts are incorrectly identified as similar to others without clearly establishing partial duplication, the plagiarism detection process becomes ineffective. Therefore, in our case, the model's performance will primarily be evaluated based on recall and F1 score, as they better reflect the system's ability to identify actual duplicates without compromising reliability. The hybrid method for near-duplicate detection aligns with the specifics of agglutinative languages due to its consideration of morphological variability through normalization and semantic generalization, free word order through order-insensitive metrics, and synonym and polysemy through thematic and conceptual similarity. Future work should focus on developing embeddings for Kazakh and adapting deep language models, such as BERT, to improve accuracy. Testing on cross-lingual datasets could address challenges posed by diverse morphological structures. The method's flexibility makes it highly applicable in plagiarism detection and intelligent data analysis for Kazakh language texts, especially in academic and research contexts. Since it was very important to achieve zero false positives (FP = 0), the resulting precision remained consistently perfect (1.00) across all hold-out/test splits. Notably, even under a classical cross-validation scheme, the false positive rate would remain zero across all folds, leading to an error bar of zero for the precision metric. This strict performance was attained by applying a conservative threshold of \(\lambda_{\text{fused}}\) = 0.7. Under this setting, the model rejects any candidate pair in which at least one of the three similarity signals (MinHash, LSA, or LDA) does not exceed the specified threshold. This design reflects a conscious trade-off, prioritizing absolute certainty in positive predictions (high precision) at the expense of a moderate reduction in recall (approximately 0.73). The combination of statistical and semantic approaches provides a more comprehensive detection of textual duplicates in Kazakh texts compared to traditional methods such as N-gram analysis and TF-IDF. Although all the methods considered show a comparable F1-measure value (around 0.67), there are significant differences in the distribution of accuracy and completeness, as well as in the processing time. The analysis reveals that the N-gram approach is characterized by high accuracy, but its limited completeness indicates an inability to detect cases of paraphrasing or partial modifications. The TF-IDF model, on the other hand, exhibits high completeness, which allows efficient capture of modified content, but false positives occur at the cost of reduced accuracy.