SUN’IY INTELLEKT YORDAMIDA O‘ZBEK TILINI TARJIMA QILISHDAGI LINGVISTIK O‘ZGARISHLAR
DOI:
https://doi.org/10.65164/qewkkz90Ключевые слова:
Sun’iy intellekt, mashinali tarjima, o‘zbek tili, lingvistik transformatsiya,kamresursli til, neyron tarjima (NMT), tabiiy tilni qayta ishlash (NLP), semantik muammolarАннотация
Ushbu maqolada sun’iy intellekt asosida ishlovchi mashinali tarjima tizimlarida o‘zbek tilining lingvistik xususiyatlari va ularning transformatsiyasi tahlil qilinadi. Raqamli jamiyat sharoitida avtomatik tarjima texnologiyalarining rivojlanishi global kommunikatsiya jarayonini sezilarli darajada tezlashtirdi. Shu bilan birga, o‘zbek tilidan tarjima qilishda kam-resursli tillarda mashinali tarjima jarayonida bir qator lingvistik muammolar yuzaga kelmoqda. O‘rganishlarda shu ma’lum bo‘ldiki, o‘zbek tilining agglutinativ tuzilishi, erkin so‘z tartibi, frazeologik birliklar va semantik noaniqliklar sun’iy intellekt tizimlari uchun asosiy murakkabliklar sifatida ko‘rib chiqiladi. Shuningdek, SI asosidagi tarjima tizimlarida kontekstni to‘liq anglamaslik, so‘zma-so‘z tarjima qilish va madaniy omillarning yetarli darajada aks etmasligi kabi kamchiliklar aniqlanadi. Maqolada zamonaviy neyron tarjima modellarining (NMT) o‘zbek tiliga ta’siri hamda lingvistik transformatsiyalar - ya’ni tilning soddalashuvi, yangi konstruksiyalarning paydo bo‘lishi va raqamli muhitda tilning o‘zgarishi tahlil qilishga harakat qilindi. Mashinali tarjima sifatini oshirish uchun parallel korpuslarni kengaytirish, milliy lingvistik bazalarni yaratish va inson ishtirokidagi kam-tahrir (post-editing) jarayonlarini rivojlantirish zarurligi zaruriyati ma’lum bo‘ldi.
Библиографические ссылки
[1] Koehn, P. Neural Machine Translation. Cambridge University Press, 2020.
[2] Castilho, S., Moorkens, J., Way, A., Gaspari, F. Assessing the Quality of machine Translation. Springer,
2018.
[3] Barrault, L. et al. Findings of the 2020 Conference on Machine Translation (WMT20). Association for
Computational Linguistics, 2020.
[4] DeepL SE. DeepL Translator Research and Accuracy Reports, 2020–2024.
[5] Toral, A., Way, A. Analyse of Machine Translation Errors in NMT Systems. Machine Translation
Journal, 2015.
[6] Freitag, M. Advanced Neural Machine Translation Techniques. ACL Publications, 2022.
[7] Madrahimov, A. Sun’iy intellekt yordamida tarjima jarayonining lingvistik xususiyatlari. Toshkent:
TDPU, 2022.
[8] Abjalova, M., Rashidova, U., Yuldasheva, D. (2025). The issue of translating idioms between Uzbek
and English in natural language processing. AIP Conference Proceedings.
https://pubs.aip.org/aip/acp/article-abstract/3377/1/070002/3366697
[9] Ismailov, A. S., Shamsiyeva, G. Statistical machine translation proposal for Uzbek to English.
https://www.researchgate.net/publication/357403849. 2021.
[10] Sharipov, M., Avezmatov, I., Kuriyozov, E. Natural language processing for Uzbek and Turkmen:
Resources and challenges. IEEE. 2025.
[11] Solidjonov, D., Najmiddinov, M. Investigating linguistic errors in large language model generation of
Uzbek text. Cogent Arts & Humanities. 2026.
[12] Amridinova, D.T. O’zbekiston fan va oliy ta’lim tizimining hozirgi rivojlanish bosqichidagi
transportasiyasi.INTERNATIONAL JOURNAL OF PHILOSOPHICAL STUDIES AND SOCIAL
SCIENCES.2026
[13] Amridinova, D.T. ЦИФРОВАЯ ТРАНСФОРМАЦИЯ И МОРАЛЬНЫЙ ВЫБОР: ЭТИЧНО
ОРИЕНТИРОВАННЫЙ ИИ В ОБРАЗОВАНИИ УЗБЕКИСТАНА. International scientific and
practical conference “WORLDVIEW HORIZONS OF ARTIFICIAL INTELLIGENCE
DEVELOPMENT”.2025
