TABIIY TILNI QAYTA ISHLASH (NLP) VA SENTIMENT TAHLILI
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https://doi.org/10.5281/zenodo.20325187;
NLP, tabiy tilni qayta ishlash, kompyuter lingvistikasi, mashina o‘rganishi, transformer, mashina tarjimasi, sentment tahlili.Abstrak
Mazkur maqolada tabiiy tilni qayta ishlash (Natural Language Processing – NLP) yo‘nalishining nazariy asoslari, asosiy tushunchalari hamda amaliy qo‘llanilish sohalari yoritiladi.[1:12] NLP sun’iy intellekt va kompyuter lingvistikasining integrativ yo‘nalishi sifatida matn va nutqni avtomatik tahlil qilish, tushunish va generatsiya qilish masalalarini hal etishga qaratilgan.[7:15] Maqolada NLPning tipik vazifalari, jumladan tokenizatsiya, lemmatizatsiya, morfologik va sintaktik tahlil, nomlangan obyektlarni aniqlash, sentiment tahlili, mashina tarjimasi, savol-javob tizimlari hamda matnni qisqartirish kabi yo‘nalishlar umumlashtiriladi. Shuningdek, klassik yondashuvlardan zamonaviy chuqur o‘rganish (deep learning) va transformer arxitekturalarigacha bo‘lgan rivojlanish tendensiyalari hamda ta’lim, tibbiyot, bank-moliya, media monitoring, call-markazlar va davlat xizmatlarida NLP yechimlarining ahamiyati tahlil qilinadi.[5:60] Tadqiqot natijasida NLP texnologiyalarini joriy etishda ma’lumotlar sifati, til resurslari, etik masalalar va xavfsizlik talablarini hisobga olish zarurligi asoslab beriladi.Iqtiboslar
Jurafsky D., Martin J. H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. – 3rd ed. – Draft version. – Stanford : Stanford University, 2023. – 716 p.[1:12]
Manning C. D., Schütze H. Foundations of Statistical Natural Language Processing. – Cambridge : MIT Press, 1999. – 680 p.[7:15]
Bird S., Klein E., Loper E. Natural Language Processing with Python. – Sebastopol : O‘Reilly Media, 2009. – 504 p.
Goodfellow I., Bengio Y., Courville A. Deep Learning. – Cambridge : MIT Press, 2016. – 775 p.[5:60]
Vaswani A., Shazeer N., Parmar N., et al. Attention Is All You Need // Advances in Neural Information Processing Systems (NeurIPS). – 2017. – Vol. 30. – P. 5998–6008.[4:120]
Devlin J., Chang M. W., Lee K., Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding // Proceedings of NAACL-HLT. – 2019. – P. 4171–4186.[1:78]
Goldberg Y. Neural Network Methods in Natural Language Processing. – San Rafael : Morgan & Claypool Publishers, 2017. – 309 p.[7:210]
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