Comparative analysis of methods of automated classification of poetic texts based on lexical signs

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Comparative analysis of methods of automated classification of poetic texts based on lexical signs. / Barakhnin, V. B.; Kozhemyakina, O. Yu; Pastushkov, I. S.

в: CEUR Workshop Proceedings, Том 2022, 2017, стр. 252-257.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Author

Barakhnin, V. B. ; Kozhemyakina, O. Yu ; Pastushkov, I. S. / Comparative analysis of methods of automated classification of poetic texts based on lexical signs. в: CEUR Workshop Proceedings. 2017 ; Том 2022. стр. 252-257.

BibTeX

@article{ca7b7306a4e041b48205c771af443929,

title = "Comparative analysis of methods of automated classification of poetic texts based on lexical signs",

abstract = "In this paper we analyze the principles of formation of the training samples for the algorithms of the definition of styles and genre types. The computational experiments with a corpus of texts of Lyceum lyrics of A. S. Pushkin at the choice of the most accurate algorithm of classification of poetic texts were conducted, including the usage of the best-known methods of assembling of the basic algorithms in the composition, such as weighted voting, boosting and stacking, and as a characteristic feature of the poems the single words, bigrams and trigrams were used. The considered algorithms showed their efficiency and can be used to automate the complex analysis of Russian poetic texts, significantly facilitating the work of the expert in determining of their styles and genres by providing the appropriate recommendations.",

keywords = "Automated analysis of poetic texts, Classification algorithms, The definition of genres and styles",

author = "Barakhnin, {V. B.} and Kozhemyakina, {O. Yu} and Pastushkov, {I. S.}",

year = "2017",

language = "English",

volume = "2022",

pages = "252--257",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

publisher = "CEUR-WS",

}

RIS

TY - JOUR

T1 - Comparative analysis of methods of automated classification of poetic texts based on lexical signs

AU - Barakhnin, V. B.

AU - Kozhemyakina, O. Yu

AU - Pastushkov, I. S.

PY - 2017

Y1 - 2017

N2 - In this paper we analyze the principles of formation of the training samples for the algorithms of the definition of styles and genre types. The computational experiments with a corpus of texts of Lyceum lyrics of A. S. Pushkin at the choice of the most accurate algorithm of classification of poetic texts were conducted, including the usage of the best-known methods of assembling of the basic algorithms in the composition, such as weighted voting, boosting and stacking, and as a characteristic feature of the poems the single words, bigrams and trigrams were used. The considered algorithms showed their efficiency and can be used to automate the complex analysis of Russian poetic texts, significantly facilitating the work of the expert in determining of their styles and genres by providing the appropriate recommendations.

AB - In this paper we analyze the principles of formation of the training samples for the algorithms of the definition of styles and genre types. The computational experiments with a corpus of texts of Lyceum lyrics of A. S. Pushkin at the choice of the most accurate algorithm of classification of poetic texts were conducted, including the usage of the best-known methods of assembling of the basic algorithms in the composition, such as weighted voting, boosting and stacking, and as a characteristic feature of the poems the single words, bigrams and trigrams were used. The considered algorithms showed their efficiency and can be used to automate the complex analysis of Russian poetic texts, significantly facilitating the work of the expert in determining of their styles and genres by providing the appropriate recommendations.

KW - Automated analysis of poetic texts

KW - Classification algorithms

KW - The definition of genres and styles

UR - http://www.scopus.com/inward/record.url?scp=85040687697&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85040687697

VL - 2022

SP - 252

EP - 257

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -

ID: 9181035