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Quantitative Estimation of Intelligibility of Foreign Slavic Languages: Case of Russian Native Speakers

https://doi.org/10.25205/1818-7900-2021-19-1-61-79

Abstract

In this article, we investigate the issue of intelligibility of a foreign Slavic text for a Russian-speaking person which don’t know this language. The aim of this article is to find out what is the percentage of intelligible words in foreign text for such a person. As a main measuring tool, we used parallel cloze tests with omitted words in the Russian part. The task was to restore omitted words using the foreign part of a test (written in Ukrainian, Belorussian, Polish, Czech, Slovak, Serbian, Slovene, and Bulgarian languages) as a clue. As a baseline, we used a control group which solved a test without the foreign part. Our hypothesis was that the foreign text intelligibility could be defined as a difference between the mean percentage of correctly restored words for a group used a parallel text and the same percentage for a control group. The results of our experiments proved our hypothesis. All the pairs “omitted word - its translation” was divided into four groups: full and partial cognates, genetic cognates, non-cognates and false friends. The correlation between the mean intelligibility of a text in a given foreign language and the percentage of full and partial cognates was as high as 0.7; the same correlation for the other word groups was negative but not so deep. Therefore, we can state that the foreign text intelligibility is defined by the percentage of full and partial cognates but that is not the only parameter. The gathered data, containing the used tests, users’ answers and their background, and the software for its analysis is placed at https://github.com/klyshinsky/mutual_intelligibility_Russian.

About the Author

E. S. Klyshinsky
Keldysh Institute of Applied Mathematics RAS
Russian Federation


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Review

For citations:


Klyshinsky E.S. Quantitative Estimation of Intelligibility of Foreign Slavic Languages: Case of Russian Native Speakers. Vestnik NSU. Series: Information Technologies. 2021;19(1):61-79. (In Russ.) https://doi.org/10.25205/1818-7900-2021-19-1-61-79

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ISSN 1818-7900 (Print)
ISSN 2410-0420 (Online)