ATOQLI OTLARNI ANIQLASHNING ANNOTATSIYA QOIDALARI VA MATEMATIK MODELLARI
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Ushbu maqolada matnlardagi atoqli otlarni (Named Entity) aniqlash uchun annotatsiya qoidalari,
BIO markalash tizimi, matematik modellar (CRF, BiLSTM-CRF, Transformer), agglutinativ
tillarga xos xususiyatlar, hamda real O‘zbek matnlari misolida amaliy misollar bayon qilinadi.
Model qurilishining formal ifodasi, ehtimollik asosidagi yondashuv, annotatorlar o‘rtasidagi kelishuv
(Cohen’s Kappa) va annotatsiya sifatini oshirish bo‘yicha usullar ham yoritiladi. Maqola natural
tilni qayta ishlash (NLP) yo‘nalishida NER tizimi yaratish istagidagi tadqiqotchilar uchun metodik
qo‘llanma sifatida xizmat qiladi.
1. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for
Named Entity Recognition. NAACL-HLT. (BiLSTM-CRF asosidagi mashhur NER modeli)
2. Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF Models for Sequence Tagging.
arXiv:1508.01991. (NER va boshqa tegishli masalalar uchun LSTM-CRFning klassik varianti)
3. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding. NAACL-HLT. (Transformer asosida kontekstli model –
zamonaviy NERning asosi)
4. Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing. Prentice Hall. (NLP bo‘yicha eng
mashhur darslik, NER bo‘limi mavjud)
5. Rajabov J.Sh., Formalizing the Uzbek Language: A Comprehensive Exploration Using Backus-Naur
Forms, Acta NUUz, vol. 1(1), 2023 (01.00.00. - 8).
Mulkiiyat (c) 2025 «O‘zMU XABARLARI»
Ushbu ish quyidagi litsenziya asosida ruxsatlangan Kreativ Commons Attribution-NonCommercial-ShareAlike 4.0 International litsenziyasi asosida bu ish ruxsatlangan..
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