Қазақ ұлттық қыздар педагогикалық университеті / Казахский национальный женский педагогический университет

Постоянный URI для этого сообществаhttps://cris.qyzpu.kz/handle/123456789/1

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2021 - 20243

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Язык: АнглийскийТема: search.filters.subject.language contact

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    Тип записи:Публикация,
    Speech Interference Generated by Proverbs and Phraseological Units of the Russian and Chinese Languages
    (2021-08-01)
    Aishagul Akbembetova
    ;
    Gulnar Bekkozhanova
    ;
    Nurzhamal Shengelbayeva
    ;
    Nuraisha Bekeyeva
    ;
    Saule Askarova
    To express their thoughts in Russian, students should have language resources in their active vocabulary, the consolidation of which in memory is advisable based on the skills of their native language. The most important role in teaching students a non-native language is played by transfer, which is one of the central categories of psychology. Russian and Chinese mentalities have different, sometimes opposite features, which make it difficult for Chinese students to learn Russian. The phenomenon of transfer occurs and exists during language contact regardless of the will and desire of bilinguals. As a result of the transfer phenomenon, interference and its cause are that certain areas in the linguistic picture of the world are preserved, to which the proverb and phraseology belong. The authors believe that proverbs should be attributed to the category of fixed expressions of the language and studied from the standpoint of general, particular, and comparative phraseology.
    Цитирования (OpenAlex) 2
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    Diachronic changes in the vocabulary of the English language
    (2023-01-01)
    Odanova Sagira Amangeldiyevna
    ;
    G.K. Abdirasilova
    ;
    Fazylakhmetova Aigul Tanatkanovna
    ;
    Darkenbayeva Zhaidargul Kadyrovna
    ;
    Satbekova Aigyl Amantaevna
    This article discusses diachronic changes in the vocabulary of the English language. The language system is constantly moving and evolving. The most mobile level is the lexical system since it is it that primarily reacts to all changes in society. In this article, the correlation between the original, proper English, and borrowed vocabulary of the English language in the process of its historical development is studied on the material of the lexicographic sources studied. The qualitative-quantitative dynamics of the variation of borrowed vocabulary are analyzed, taking into account the source languages within the considered periods of the development of the vocabulary of the English language. The most priority socially determined lexico-semantic group of the main parts of speech is presented, taking into account their dominance in various periods of the development of the English language; Also, in this article, an etymological analysis of the main parts of speech in the English language is carried out, and general and particular trends of their etymological development are identified in this lexicographic material.
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    ANALYZING RUSSIAN BORROWINGS IN KAZAKH SOCIAL MEDIA USING MACHINE LEARNING
    (2024-12-24)
    Saya Sapakova
    ;
    Б. Ғ. Қамбарова
    This study explores the influence of Russian words on the development of the Kazakh language in social networks. The rapid advancement of information technology significantly impacts the language used in online communications. While the chaotic nature of online interactions can complicate language use and create confusion, it also accelerates the spread of information in Kazakh. This research examines how foreign words affect modern Kazakh internet discourse, including direct borrowings that enter the language without modification, mixedphrases that retain the lexical and semantic properties of foreign words, the emergence of new abbreviations, and the influence of barbarisms. The study utilizes machine learning methods to analyse social media content from Instagram and Facebook. This approach enabled the processing of over 100,000 posts, revealing key linguistic shifts associated with the integration of Russian borrowings into Kazakh. The use of machine learning algorithms, such as the Naive Bayes classifier, automated the data analysis process and uncovered hidden patterns, providing a deeper understanding of how these borrowings affect the Kazakh language in the digital environment.