kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-4-227-243kaz29-2296Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEВЫЧИСЛИТЕЛЬНЫЙ КОНВЕЙЕР ДЛЯ ЛЕКСИЧЕСКОГО И ТЕМАТИЧЕСКОГО АНАЛИЗА КОДЕКСА ОБ АДМИНИСТРАТИВНЫХ ПРАВОНАРУШЕНИЯХ РЕСПУБЛИКИ КАЗАХСТАНA COMPUTATIONAL PIPELINE FOR LEXICAL AND THEMATIC ANALYSIS OF THE CODE OF ADMINISTRATIVE OFFENSES OF THE REPUBLIC OF KAZAKHSTANhttps://orcid.org/0009-0008-4606-3628МухсимбаевБ.MukhsimbayevB.

докторант

г. Алматы

PhD student

Almaty

b.mukhsimbaev@kbtu.kzhttps://orcid.org/0000-0002-8685-9355ПакА.PakA.

PhD, профессор

г. Алматы

PhD, Professor

Almaty

a.pak@kbtu.kzhttps://orcid.org/0009-0001-0811-5385КуралбаевА.KuralbayevA.

докторант

г. Алматы

PhD student

Almaty

a.kuralbaev@kbtu.kzКазахстанско-Британский технический университетКазахстанKazakh-British Technical UniversityKazakhstan202523122025224227243Copyright © Мухсимбаев Б., Пак А., Куралбаев А., 20252025Мухсимбаев Б., Пак А., Куралбаев А.Mukhsimbayev B., Pak A., Kuralbayev A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2296

В статье представлен вычислительный конвейер для автоматизированного лингвистического и структурного анализа юридических текстов на примере Кодекса Республики Казахстан об административных правонарушениях (КоАП РК, K1400000235). Предложенный рабочий процесс объединяет этапы сбора данных, предобработки текста, токенизации, извлечения ключевых слов, семантической кластеризации и визуализации с применением методов обработки естественного языка (NLP) и статистического анализа на Python. Разработанный конвейер сочетает лексический, тематический и количественный лингвистический анализ в единую последовательную систему, что позволяет выявлять частотные распределения, семантические поля и скрытые темы в иерархической структуре Кодекса (разделы, главы, статьи). Анализ корпуса КоАП РК выявил ряд характерных языковых закономерностей: преобладание лексики, связанной с санкциями и ответственностью (штраф, ответственность, правонарушение), повышенную лексическую плотность в главах, регулирующих экономические и процессуальные правонарушения, а также тематические кластеры, отражающие нормативно-карательную направленность административного права. Визуализационные методы, такие как частотные гистограммы, тематические тепловые карты и топик-карты, демонстрируют потенциал конвейера для количественного исследования законодательного языка. В целом представленная методология формирует масштабируемую основу для сравнительной юридической лингвистики, автоматизированного мониторинга законодательства и модернизации правовой аналитики в Казахстане.

This study introduces a computational pipeline for the automated linguistic and structural analysis of legal texts, applied to the Code of Administrative Offenses of the Republic of Kazakhstan (CAO RK, K1400000235). The proposed workflow integrates data collection, text preprocessing, tokenization, keyword extraction, semantic clustering, and visualization using natural language processing (NLP) and statistical techniques implemented in Python. The pipeline unites lexical, thematic, and quantitative linguistic analyses into a coherent sequence that enables the identification of frequency distributions, semantic fields, and latent topics across the hierarchical structure of the Code (sections, chapters, and articles). The analysis of the CAO RK corpus revealed several distinctive linguistic patterns: a dominance of sanction and responsibility-related vocabulary (штраф, ответственность, правонарушение), high lexical density in chapters regulating economic and procedural offenses, and concentrated thematic clusters reflecting the normative-punitive orientation of administrative law. Visualization techniques such as frequency histograms, thematic heatmaps, and topic maps illustrate the potential of the pipeline for exploring legislative language quantitatively. Overall, the framework establishes a scalable foundation for comparative legal linguistics, automated legislative monitoring, and the modernization of legal analytics in Kazakhstan.

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The authors declare that there are no conflicts of interest present.