maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2021-3-280-291mais-1528Research ArticleTheory of DataКлассификация текстов по жанрам на основе ритмических характеристикText Classification by Genre Based on Rhythm Featureshttps://orcid.org/0000-0002-1742-3240ЛагутинаКсения ВладимировнаLagutinaKsenia Vladimirovna

Аспирант.

Ул. Советская, д. 14, Ярославль, 150003

Postgraduate student.

14 Sovetskaya str., Yaroslavl 150003

lagutinakv@mail.ruhttps://orcid.org/0000-0002-6137-8643ЛагутинаНадежда СтаниславовнаLagutinaNadezhda Stanislavovna

Канд. физико-математических наук, доцент.

Ул. Советская, д. 14, Ярославль, 150003

PhD, associate professor.

14 Sovetskaya str., Yaroslavl 150003

lagutinans@rambler.ruhttps://orcid.org/0000-0001-6600-2971БойчукЕлена ИгоревнаBoychukElena Igorevna

Доктор философский наук, доцент.

Ул. Республиканская, д. 108/1, Ярославль, 150000

PhD, associate professor.

108/1 Respublikanskaya str., Yaroslavl 150000

elena-boychouk@rambler.ruЯрославский государственный университет им. П.Г. ДемидоваРоссияP.G. Demidov Yaroslavl State UniversityRussian FederationЯрославский государственный педагогический университет им. К.Д. УшинскогоРоссияYaroslavl State Pedagogical University named after K.D. UshinskyRussian Federation202112102021283280291Copyright © Лагутина К.В., Лагутина Н.С., Бойчук Е.И., 20212021Лагутина К.В., Лагутина Н.С., Бойчук Е.И.Lagutina K.V., Lagutina N.S., Boychuk E.I.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/1528

Статья посвящена анализу ритма текстов различных жанров: художественных романов, рекламы, научных статей, отзывов, твитов и политических статей. Авторы выделили в текстах лексико-грамматические средства: анафору, эпифору, диакопу, апозиопезу и т. п., которые являются маркерами ритма текста. На их основе были подсчитаны статистические характеристики, описывающие количественно и структурно данные ритмические средства.

Полученная модель текста была визуализирована для статистического анализа с помощью диаграмм размаха и тепловых карт, которые показали отличия в ритме текстов различных жанров. Диаграммы размаха показали, что практически все жанры отличаются друг от друга по общей плотности ритмических характеристик. Тепловые карты показали различную структуру ритма у жанров.

Далее ритмические характеристики успешно использовались для классификации текстов по шести жанрам. Высокое качество классификации показало, что ритмические характеристики являются хорошим маркером для большинства жанров, в особенности для художественной литературы. Эксперименты проводились с помощью программного инструмента ProseRhythmDetector для русского и английского языков. Корпуса текстов содержат по 300 текстов для каждого языка.

The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features.

The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language.

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