TABIIY TILNI QAYTA ISHLASH (NLP) SOHASINING SHAKLLANISHI VA RIVOJLANISH BOSQICHLAR
##submission.downloads##
Ushbu maqolada tabiiy tilni qayta ishlash sohasining shakllanishi va rivojlanish bosqichlari tizimli ravishda tahlil qilinadi.
Tadqiqotda NLP tarixining asosiy evolyutsion davrlari, qoidalarga assolangan va grammatik yondashuvlar, semantik modellar,
statistik usullar hamda chuqur o‘rganish va katta oldindan o‘rgatilgan til modellari bosqichlari ilmiy manbalar asosida yoritiladi.
Adabiyotlar sharhi jarayonida mashina tarjimasining dastlabki g‘oyalari, kontseptual ontologiyalar, korpusga asoslangan statistik
modellar va zamonaviy neyron arxitekturalarning shakllanishi ketma-ketlikda tahlil qilinadi. Shuningdek, attention mexanizmi,
Transformer arxitekturasi va transfer learning yondashuvlarining NLP rivojiga qo‘shgan hissasi ko‘rsatib beriladi. O‘tkazilgan
tahlil zamonaviy NLP tizimlarining nazariy va metodologik asoslarini aniqlash hamda kam resursli tillar, xususan o‘zbek tili uchun
samarali avtomatik matn tahlili modellarini ishlab chiqishda muhim ilmiy asos bo‘lib xizmat qiladi.
1. Liddy E.D. Natural Language Processing // Encyclopedia of Library and Information Science. – New York: Marcel Dekker,
2001. – P. 2126–2136.
2. Thistlethwaite F., Dostert L. The Great Experiment: Machine Translation. – Washington, DC, 1955. – 98 p.
3. Charniak E. Passing markers: A theory of contextual influence in language comprehension // Cognitive Science. – 1983. –
Vol. 7, No. 3. – P. 171–190.
4. Brown P.F. et. al. Word-sense disambiguation using statistical methods // Proceedings of the 29th Annual Meeting of the
Association for Computational Linguistics. – 1991. – P. 264–270.
5. Bengio Y. et. al. A neural probabilistic language model // Journal of Machine Learning Research. – 2003. – Vol. 3. – P. 1137–
1155.
6. Bahdanau D. et. al. Neural machine translation by jointly learning to align and translate // arXiv preprint. – 2014. –
arXiv:1409.0473.
7. Daniluk M. et. al. Frustratingly short attention spans in neural language modeling // arXiv preprint. – 2017. –
arXiv:1702.04521.
8. Collobert R., Weston J. A unified architecture for natural language processing // Proceedings of the 25th International
Conference on Machine Learning (ICML). – 2008. – P. 160–167.
9. Socher R. et. al. Recursive deep models for semantic compositionality over a sentiment treebank // Proceedings of EMNLP
2013. – P. 1631–1642.
10. Tai K.S. et. al. Improved semantic representations from tree-structured LSTM networks // arXiv preprint. – 2015. –
arXiv:1503.00075.
11. Sutskever I. et. al. Sequence to sequence learning with neural networks // Advances in Neural Information Processing
Systems. Vol. 27. 2014. – P. 3104–3112.
12. Devlin J. et. al. BERT: Pre-training of deep bidirectional transformers for language understanding // Proceedings of NAACL-
HLT 2019. – P. 4171–4186.
Mulkiiyat (c) 2026 «O‘zMU XABARLARI»
Ushbu ish quyidagi litsenziya asosida ruxsatlangan Kreativ Commons Attribution-NonCommercial-ShareAlike 4.0 International litsenziyasi asosida bu ish ruxsatlangan..
.jpg)
.png)
