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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">lingngu</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник НГУ. Серия: Лингвистика и межкультурная коммуникация</journal-title><trans-title-group xml:lang="en"><trans-title>NSU Vestnik. Series: Linguistics and Intercultural Communication</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1818-7935</issn><publisher><publisher-name>Новосибирский государственный университет</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.25205/1818-7935-2026-24-1-74-86</article-id><article-id custom-type="elpub" pub-id-type="custom">lingngu-1198</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>КОМПЬЮТЕРНАЯ И ПРИКЛАДНАЯ ЛИНГВИСТИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>COMPUTER AND APPLIED LINGUISTICS</subject></subj-group></article-categories><title-group><article-title>Определение особенностей авторского стиля поэтов «озерной школы» методами машинного обучения</article-title><trans-title-group xml:lang="en"><trans-title>Determination of the «Lake School» Author Style Features by Machine Learning Methods</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3299-0507</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Барахнин</surname><given-names>В. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Barakhnin</surname><given-names>V. B.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Барахнин Владимир Борисович, доктор технических наук, заведующий лабораторией информационных ресурсов; заведующий кафедрой математического моделирования</p></bio><bio xml:lang="en"><p>Vladimir B. Barakhnin, Doctor of Technical Sciences, Head of the Laboratory of Information Resources; Head of the Department of Mathematical Modeling</p></bio><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Чжэньдун</surname><given-names>Чжао</given-names></name><name name-style="western" xml:lang="en"><surname>Zhendong</surname><given-names>Zhao</given-names></name></name-alternatives><bio xml:lang="ru"><p>Чжао Чжэньдун, выпускник бакалавриата; магистрант </p></bio><bio xml:lang="en"><p>Zhao Zhendong, Bachelor’s Degree Graduate from Novosibirsk State, Master’s Student at Moscow State University</p></bio><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0008-5512-4760</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мачикина</surname><given-names>Е. П.</given-names></name><name name-style="western" xml:lang="en"><surname>Machikina</surname><given-names>E. P.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Мачикина Елена Павловна, кандидат физико-математических наук, старший научный сотрудник; доцент кафедры прикладной математики и кибернетики </p></bio><bio xml:lang="en"><p>Elena P. Machikina, Candidate of Physical and Mathematical Sciences, Senior Researcher; Associate Professor of the Department of Applied Mathematics and Cybernetics</p></bio><xref ref-type="aff" rid="aff-3"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Федеральный исследовательский центр информационных и вычислительных технологий; Новосибирский государственный университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Federal Research Center for Information and Computing Technologies; Novosibirsk State University</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Новосибирский государственный университет; Московский государственный университет им. М. В. Ломоносова</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Novosibirsk State University; Moscow State University named after M. V. Lomonosov</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-3"><aff xml:lang="ru"><institution>Федеральный исследовательский центр информационных и вычислительных технологий; Сибирский государственный университет телекоммуникаций и информатики</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Federal Research Center for Information and Computing Technologies; Siberian State University of Telecommunications and Informatics</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>27</day><month>05</month><year>2026</year></pub-date><volume>24</volume><issue>1</issue><fpage>74</fpage><lpage>86</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Барахнин В.Б., Чжэньдун Ч., Мачикина Е.П., 2026</copyright-statement><copyright-year>2026</copyright-year><copyright-holder xml:lang="ru">Барахнин В.Б., Чжэньдун Ч., Мачикина Е.П.</copyright-holder><copyright-holder xml:lang="en">Barakhnin V.B., Zhendong Z., Machikina E.P.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://lingngu.elpub.ru/jour/article/view/1198">https://lingngu.elpub.ru/jour/article/view/1198</self-uri><abstract><p>Статья посвящена исследованию стилистических особенностей поэзии представителей «озерной школы». Методология исследования включала в себя несколько этапов. Первоначально был сформирован англоязычный корпус текстов, состоящий из 99 поэтических произведений трех авторов, принадлежащих к данному литературному направлению. На данном этапе была осуществлена предварительная обработка текстов и извлечение релевантных признаков. Далее для представления поэтических текстов была применена модель пространства текстовых векторов. С целью анализа и идентификации уникальных стилистических характеристик каждого поэта была использована матрица признаков, проанализированная с применением пяти распространенных алгоритмов машинного обучения, включая метод опорных векторов (SVM) и случайный лес (Random Forest). Для решения проблемы несбалансированности данных была применена техника оверсэмплинга SMOTE (Synthetic Minority Oversampling Technique), что позволило повысить точность классификации. В частности, модифицированная модель SVM достигла F1-меры, превышающей 90 %, в задаче распознавания авторства поэтических текстов. На заключительном этапе исследования было проведено сопоставление комплексных стилистических характеристик поэзии трех авторов, что позволило выявить значимые различия в их индивидуальных стилях. Результаты исследования предоставляют ценную информацию для дальнейшего изучения в области анализа стилистических особенностей поэзии.</p></abstract><trans-abstract xml:lang="en"><p>The article is devoted to the study of stylistic features of the Lake School poets. The research methodology included several stages. Initially, a corpus of texts was formed, consisting of 99 poetic works by three authors belonging to this literary trend. At this stage, preliminary text processing and extraction of relevant features were carried out. Further, a model of text vectors space was applied to represent poetic texts. In order to analyze and identify the unique stylistic characteristics of each poet, a feature matrix was applied, analyzed using ﬁve common machine learning algorithms, including the support vector machine (SVM) method and Random Forest. To solve the problem of data imbalance, SMOTE (Synthetic Minority Oversampling Technique) was applied, which improved classiﬁcation accuracy. In particular, the modiﬁed SVM model achieved an F1 measure exceeding 90 % in the task of recognizing the authorship of poetic texts. At the ﬁnal stage of the study, the complex stylistic characteristics of the poetry by the three authors were compared, which revealed signiﬁcant diﬀerences in their individual styles. The results of the study provide valuable information for further research in the ﬁeld of poetry stylistic features.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>стилометрия</kwd><kwd>машинное обучение</kwd><kwd>классификация</kwd><kwd>идентификация авторства</kwd><kwd>технология SMOTE</kwd></kwd-group><kwd-group xml:lang="en"><kwd>stylometry</kwd><kwd>machine learning</kwd><kwd>classiﬁcation</kwd><kwd>authorship identiﬁcation</kwd><kwd>SMOTE technology</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Андреев В. С. Классификация стихотворных текстов методом дискриминантного анализа // Математическая морфология: электронный математический и медико-биологический журнал. 2003. Т. 5, № 1. С. 58–70.</mixed-citation><mixed-citation xml:lang="en">Andreev V. S. Classiﬁcation of Verse Texts by Means of Discriminant Analysis. 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