Pasternak lyrics: part of speech structure

IF 0.2 Q4 LINGUISTICS
Glottometrics Pub Date : 2021-01-01 DOI:10.53482/2021_51_391
S. Andreev
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引用次数: 0

Abstract

The article is devoted to the study of the stability and variability of part of speech structures in the collections of lyrical poems by B. Pasternak, the Nobel Prize winner for literature. The analysis is based on the methodology proposed by Gabriel Altmann in his studies. The database includes 7 collections of Pasternak’s lyrics, published by him over the period of more than 40 years. The study was carried out on the material of both individual poems and the framework of entire collections. The results obtained showed that in Pasternak's lyrics, nominality of texts is very high. Within the framework of each separate collection a high stability of the general structure of parts of speech was observed. Dynamic description was found to prevail over static description. It was found that both types of description are guided by the tendency to compensation when the growth of one of them causes a decrease in the other. It was discovered that the distribution of parts of speech within each collection of lyrics is very well fitted by the Zipf-Alekseev function. Using the Euclidean distances between the collections of lyrical poems, published during different periods of the author’s creative work, assumptions were made about possible stages of the author's style evolution.
帕斯捷尔纳克歌词:言语结构的一部分
本文对诺贝尔文学奖得主帕斯捷尔纳克抒情诗集中词性结构的稳定性和变异性进行了研究。该分析基于Gabriel Altmann在他的研究中提出的方法。该数据库包括帕斯捷尔纳克在40多年的时间里出版的7本歌词集。这项研究既对个别诗歌的材料进行了研究,也对整个诗集的框架进行了研究。结果表明,在帕斯捷尔纳克的歌词中,文本的名义性非常高。在每个单独集合的框架内,观察到词类总体结构的高度稳定性。动态描述优于静态描述。研究发现,当其中一种的增长导致另一种的减少时,这两种类型的描述都受到补偿倾向的指导。我们发现,词性分布在每一个歌词集合是非常适合的Zipf-Alekseev函数。利用作者在不同创作时期出版的抒情诗集之间的欧几里得距离,对作者风格演变的可能阶段进行了假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Glottometrics
Glottometrics LINGUISTICS-
CiteScore
0.50
自引率
0.00%
发文量
0
期刊介绍: The aim of Glottometrics is quantification, measurement and mathematical modeling of any kind of language phenomena. We invite contributions on probabilistic or other mathematical models (e.g. graph theoretic or optimization approaches) which enable to establish language laws that can be validated by testing statistical hypotheses.
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