Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Methods of Automatic Selection of Named Entities (NER) in Uzbek Language for Text Tone Analysis. / Saidov, Bobur R.; Barakhnin, Vladimir B.; Rixsibayev, Ulugbek T. и др.
International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2025. стр. 1740-1745 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Methods of Automatic Selection of Named Entities (NER) in Uzbek Language for Text Tone Analysis
AU - Saidov, Bobur R.
AU - Barakhnin, Vladimir B.
AU - Rixsibayev, Ulugbek T.
AU - Sobirov, Ogabek O.
AU - Bekchanov, Khikmat M.
AU - Sharipov, Elbek J.
N1 - Conference code: 26
PY - 2025/8/8
Y1 - 2025/8/8
N2 - This paper investigates integrated approaches for automatic detection of named entities (Named Entity Recognition, NER) and sentiment analysis in Uzbek texts. The development proposes an architecture that combines NER and sentiment analysis, taking into account the morphological features of the Uzbek language and the lack of a sufficiently annotated database. The study performed named entity detection using the BiLSTM+CRF model (F1=0.82) and sentiment analysis using the Uzbek-tuned XLM-RoBERTa model (accuracy=89%). The results showed that the NER system allows for more accurate context analysis in sentiment assessment. In particular, geographical entities such as 'Tashkent city' were evaluated as positive with 95% accuracy, and sentences such as 'The new performance at the Navoi Theater was great' were evaluated as positive with 91% reliability. At the same time, dialectical expressions and ambiguous words created difficulties in the analysis. The results of the study can serve as a basis for improving NER and tonality analysis in Uzbek. It is recommended that future work be carried out to adapt transformer models to the Uzbek language and expand annotated corpora.
AB - This paper investigates integrated approaches for automatic detection of named entities (Named Entity Recognition, NER) and sentiment analysis in Uzbek texts. The development proposes an architecture that combines NER and sentiment analysis, taking into account the morphological features of the Uzbek language and the lack of a sufficiently annotated database. The study performed named entity detection using the BiLSTM+CRF model (F1=0.82) and sentiment analysis using the Uzbek-tuned XLM-RoBERTa model (accuracy=89%). The results showed that the NER system allows for more accurate context analysis in sentiment assessment. In particular, geographical entities such as 'Tashkent city' were evaluated as positive with 95% accuracy, and sentences such as 'The new performance at the Navoi Theater was great' were evaluated as positive with 91% reliability. At the same time, dialectical expressions and ambiguous words created difficulties in the analysis. The results of the study can serve as a basis for improving NER and tonality analysis in Uzbek. It is recommended that future work be carried out to adapt transformer models to the Uzbek language and expand annotated corpora.
KW - BiLSTM
KW - Uzbek language
KW - XLM-RoBERTa
KW - named object recognition (NER)
KW - natural language processing
KW - tonality analysis
UR - https://www.scopus.com/pages/publications/105014143295
UR - https://www.mendeley.com/catalogue/858384ba-87f8-3afc-bd6d-32b5fff24717/
U2 - 10.1109/EDM65517.2025.11096748
DO - 10.1109/EDM65517.2025.11096748
M3 - Conference contribution
SN - 9781665477376
T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
SP - 1740
EP - 1745
BT - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
PB - IEEE Computer Society
T2 - 2025 IEEE 26th International Conference of Young Professionals in Electron Devices and Materials (EDM)
Y2 - 27 June 2025 through 1 July 2025
ER -
ID: 68948944