RUS TILIDAGI CHET TILIDAGI KOLLOKATSIYALARNI RUS TILINING ONA TILI PORTRETI VA ESKIZ MEXANIZMIDAN FOYDALANGAN HOLDA O'RGANISH

KIRISH: kollokatsiyalarni o'rganish rus tilini chet tili sifatida (RFL) o'qitishda muhim rol o'ynaydi. Kollokatsiyalar til o'rganuvchilarga so'zlarni to'g'ri birlashtirishga va tabiiy nutqni rivojlantirishga yordam beradi. Zamonaviy korpus texnologiyalari leksik kollokatsiyalarni o'rganish uchun yangi imkoniyatlarni taqdim etadi. Rus tilining milliy korpusi (NACR) va Sketch Engine platformasi kabi resurslar real kontekstlarda so'zlardan foydalanishni tahlil qilish va eng tipik kollokatsiyalarni aniqlash imkonini beradi. "So'z portreti" vositasidan foydalanish leksik munosabatlarni va tildagi so'z funksiyasining o'ziga xos xususiyatlarini chuqurroq tushunishga yordam beradi. MAQSAD: ushbu tadqiqotning maqsadi Rossiya Milliy Korpusi va Sketch Engine tizimidagi "so'z portreti" vositasidan foydalangan holda rus tilini chet tili sifatida o'qitishda kollokatsiyalarni o'rganish, shuningdek, ularning o'quvchilarning leksik kompetentsiyasini rivojlantirishdagi samaradorligini aniqlashdir. MATERIALLAR VA USULLAR: tadqiqot ma'lumotlari Rossiya Milliy Korpusi va Sketch Engine korpusidan olingan kollokatsiyalardan iborat edi. Tadqiqotda korpus tahlili, leksik-semantik tahlil, qiyosiy tahlil va tavsif usuli qo'llanilgan. Tahlilda "so'z portreti" va odatiy iboralarni avtomatik qidirish qo'llanilgan. NATIJALAR VA MUHOKAMA: tadqiqot natijalari shuni ko'rsatdiki, korpus vositalaridan foydalanish odatiy kollokatsiyalarni aniqlashni sezilarli darajada osonlashtiradi va til o'rganuvchilarga so'z birikmalari naqshlarini yaxshiroq tushunishga yordam beradi. "So'z portreti" vositasi eng ko'p uchraydigan iboralarni, grammatik munosabatlarni va foydalanish kontekstlarini aniqlash imkonini beradi. Sketch Engine platformasi kollokatsiya tahlili va statistik ma'lumotlarni qayta ishlash uchun ilg'or imkoniyatlarni taqdim etadi. Ushbu resurslardan rus tilini chet tili sifatida o'qitishda (RFL) foydalanish leksik kompetentsiyani rivojlantirishga yordam beradi, to'g'ri kollokatsiyalarni tuzish qobiliyatini yaxshilaydi va tilni bilish darajasini oshiradi. XULOSA: shunday qilib, rus tilining milliy korpusi (NCRY) va Sketch Engine kabi korpusga asoslangan texnologiyalardan foydalanish rus tilini chet tili sifatida o'qitishda kollokatsiyalarni o'rganish uchun samarali vositadir. Ular til materiallarini tartibga solishga, talabalarning so'z boyligini kengaytirishga va kontekstda so'zlarni to'g'ri ishlatish ko'nikmalarini rivojlantirishga yordam beradi.

INTRODUCTION: the study of collocations plays an important role in the teaching of Russian as a foreign language (RFL). Collocations help language learners to combine words correctly and produce natural speech. Modern corpus technologies open new opportunities for investigating lexical combinability. Resources such as the National Corpus of the Russian Language (NCRL) and the Sketch Engine platform allow for the analysis of word usage in real contexts and the identification of the most typical word combinations. The use of the “word portrait” tool facilitates a deeper understanding of lexical relationships and the functioning of words in the language. PURPOSE: the purpose of this study is to examine collocations in the teaching of Russian as a foreign language using the “word portrait” tool in the National Corpus of the Russian Language and the Sketch Engine system, as well as to assess their effectiveness in developing learners’ lexical competence. MATERIALS AND METHODS: the research material consisted of collocations extracted from the National Corpus of the Russian Language and the Sketch Engine corpus. The study employed methods of corpus analysis, lexical-semantic analysis, comparative analysis, and the descriptive method. The “word portrait” function and automatic search for typical word combinations were used for analysis. RESULTS AND DISCUSSION: the results showed that the use of corpus tools significantly facilitates the identification of typical collocations and helps learners better understand the patterns of word combination. The “word portrait” tool allows visualization of the most frequent combinations, grammatical relations, and usage contexts. The Sketch Engine platform provides advanced opportunities for collocation analysis and statistical processing of data. Applying these resources in RFL teaching contributes to the development of lexical competence, improves skills in constructing correct word combinations, and enhances overall language proficiency. CONCLUSION: thus, the use of corpus technologies such as the NCRL and Sketch Engine is an effective tool for studying collocations in the teaching of Russian as a foreign language. They allow for the systematic organization of language material, expand learners’ vocabulary, and promote the development of skills for correct word usage in context.