TABIIY TILNI QAYTA ISHLASH (NLP) VA JAVOBLAR YARATISHNI INTEGRATSIYA QILISH

Mualliflar

  • Bekzod Umarov Muallif
  • Umidaxon Mamatojiyeva Muallif

Abstrak

Mashinalarni o‘rganish (ML) va tabiiy tilni qayta ishlash (NLP) sun’iy intellektning asosiy tarmoqlaridan bo‘lib, inson tilini tushunish va qayta ishlashda katta ahamiyatga ega. NLP texnologiyalarini javoblar yaratish tizimlari bilan integratsiya qilish orqali foydalanuvchilarga tezkor va aniq javoblar berish imkoniyati yaratiladi. Ushbu maqolada NLP ning mashinalarni o‘rganish bilan bog‘liq asosiy bosqichlari, javob yaratish texnologiyalari va ularning amaliy qo‘llanilish sohalari ko‘rib chiqiladi. Shuningdek, NLP va javob yaratish texnologiyalarining kelajakdagi rivojlanish yo‘nalishlari ham tahlil qilinadi.

Iqtiboslar

Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.

Silver, D., Huang, A., & et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.

Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end trSIning of deep visuomotor policies. The Journal of Machine Learning Research, 17(1), 1334-1373.

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and BrSIn Sciences, 40, e253.

Lillicrap, T. P., & et al. (2016). Continuous control with deep reinforcement learning. International Conference on Learning Representations.

Nashr qilingan

2024-11-21

Iqtibos keltirish tartibi

TABIIY TILNI QAYTA ISHLASH (NLP) VA JAVOBLAR YARATISHNI INTEGRATSIYA QILISH. (2024). Yangi O’zbekiston Ilmiy Tadqiqotlar Jurnali, 1(13), 23-26. https://in-academy.uz/index.php/YOITJ/article/view/37483