Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Towards Effective Named Entity Recognition in Uzbek Medical Contexts. / Mengliev, Davlatyor B.; Barakhnin, Vladimir B.; Samandarova, Barno S. et al.
2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 294-298 (2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Towards Effective Named Entity Recognition in Uzbek Medical Contexts
AU - Mengliev, Davlatyor B.
AU - Barakhnin, Vladimir B.
AU - Samandarova, Barno S.
AU - Shamieva, Nargiza A.
AU - Rakhmanova, Umida U.
AU - Ibragimov, Bahodir B.
PY - 2024/11/26
Y1 - 2024/11/26
N2 - In this research work, the authors developed an algorithm for recognizing named entities adapted for medical texts in the Uzbek language. The Python's Spacy library was chosen as the main technology for implementing the algorithm, including its multilingual model for training its own (custom) model for the Uzbek language. The researchers also conducted a comparative analysis of similar works devoted to similar problems and issues, and provided objective reasons for the relevance of the proposed work. In addition, the authors also tested the algorithm with different datasets in order to identify the effectiveness of detecting named entities, including medical terminology. As a result of testing the algorithm on 1000 sentences, the average accuracy rate reached 92%, which can be indicated as a good efficiency. In the final part, the authors note possible directions for the development of the work, in particular, the use of alternative neural network architectures and a marked corpus.
AB - In this research work, the authors developed an algorithm for recognizing named entities adapted for medical texts in the Uzbek language. The Python's Spacy library was chosen as the main technology for implementing the algorithm, including its multilingual model for training its own (custom) model for the Uzbek language. The researchers also conducted a comparative analysis of similar works devoted to similar problems and issues, and provided objective reasons for the relevance of the proposed work. In addition, the authors also tested the algorithm with different datasets in order to identify the effectiveness of detecting named entities, including medical terminology. As a result of testing the algorithm on 1000 sentences, the average accuracy rate reached 92%, which can be indicated as a good efficiency. In the final part, the authors note possible directions for the development of the work, in particular, the use of alternative neural network architectures and a marked corpus.
KW - Multilingual models
KW - Natural Language Processing
KW - Spacy library
KW - Uzbek medical texts
KW - algorithm efficiency
KW - comparative analysis
KW - custom model training
KW - entity detection accuracy
KW - marked corpus
KW - medical data processing
KW - medical terminology
KW - neural network architectures
KW - text mining
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85212051290&origin=inward&txGid=e2773ea4b15bda93841ce81d9740fd61
UR - https://www.mendeley.com/catalogue/29d92ef9-97f6-3e5f-a683-1cef88dd9b0a/
U2 - 10.1109/SIBIRCON63777.2024.10758445
DO - 10.1109/SIBIRCON63777.2024.10758445
M3 - Conference contribution
SN - 9798331532024
T3 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024
SP - 294
EP - 298
BT - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Multi-Conference on Engineering, Computer and Information Sciences
Y2 - 30 September 2024 through 2 November 2024
ER -
ID: 61788211