Published September 10, 2025
| Version v1
Conference paper
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Named Entity Recognition for Confidential and Military Texts in the Uzbek Language
Authors/Creators
- 1. National University of Uzbekistan, bozorov.obid@gmail.com
Description
This thesis presents a specialized Named Entity Recognition (NER) system for identifying sensitive entities in Uzbek confidential and military texts. By fine-tuning the bertbek-ner-uznews transformer model with domain-specific synthetic data, we achieved an F1-score of 89.6% across seven custom entity categories. The system addresses critical gaps in Uzbek NLP for security applications and provides a foundation for Data Loss Prevention (DLP) integration.
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33-37.pdf
Additional details
References
- 1. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186.