TABIIY TILNI QAYTA ISHLASHDA YANGI ALGORITMLAR VA ULARNING DASTURIY TA’MINOTI

Botirova Kumush Zafar qizi; Zaripova Mukaddas Djumayozovna

Authors

Botirova Kumush Zafar qizi Termiz davlat universiteti Kompyuter tizimlari va ularning dasturiy ta’minoti mutaxassisligi 1-kurs magistranti
Zaripova Mukaddas Djumayozovna Termiz davlat universiteti Kompyuter va dasturiy injiniring kafedrasi, dotsent

Keywords:

Tabiiy tilni qayta ishlash, chuqur o‘rganish algoritmlari, transformator modellar, semantik tahlil, ko‘p tilli matnlarni qayta ishlash, algoritm samaradorligi, dasturiy integratsiya, mashina o‘rganish, kompyuter lingvistikasi, avtomatik matn generatsiyasi

Abstract

Ushbu maqola tabiiy tilni qayta ishlash (NLP – Natural Language Processing) sohasida yangi algoritmlarning rivojlanishi va ularning dasturiy ta’minoti bilan bog‘liq zamonaviy yondashuvlarni o‘rganadi. Tadqiqotda chuqur o‘rganish (deep learning), transformatorlar (transformers), va ilg‘or semantik modellar orqali matnni avtomatik tahlil qilish va generatsiya qilish imkoniyatlari tahlil qilinadi. Shuningdek, maqola algoritmlarning samaradorligi, ularning dasturiy platformalarda integratsiyasi va real dunyo masalalarini hal qilishdagi amaliy qo‘llanilishi haqida so‘z yuritadi. Tadqiqot natijalari shuni ko‘rsatadiki, yangi algoritmlar NLP tizimlarining aniqlik, samaradorlik va moslashuvchanligini sezilarli darajada oshiradi, shuningdek, tabiiy tilni qayta ishlashni talab qiladigan turli sohalarda (ta’lim, tibbiyot, biznes, ilmiy tadqiqotlar) keng qo‘llanishi mumkin.

References

1. Vasilyeva, E. (2021). Natural Language Processing with Transformers. Moscow: Science Press.

2. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

3. Radford, A., et al. (2019). Language Models are Unsupervised Multitask Learners. OpenAI.

4. Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan & Claypool Publishers.

5. Jurafsky, D., & Martin, J.H. (2022). Speech and Language Processing (3rd ed.). Pearson.

6. Brown, T., et al. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165.

7. Manning, C., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.

8. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781.

9. Peters, M., et al. (2018). Deep Contextualized Word Representations. arXiv preprint arXiv:1802.05365.

10. Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems.