TY - GEN

T1 - Dictionary-Based Medical Text Analysis in Uzbek: Overcoming the Low-Resource Challenge

AU - Mengliev, Davlatyor

AU - Barakhnin, Vladimir

AU - Eshkulov, Mukhriddin

AU - Palvanov, Bozorboy

AU - Abdurakhmonova, Nilufar

AU - Khamraeva, Saida

N1 - © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - In the dynamically developing field of computational linguistics, problems associated with the processing of low-resource languages can face to certain difficulties. Moreover, solving such a problem becomes more complicated in the context of medical text processing, where the algorithm is required to do more subtle work than simply understand the context of the source text. The article proposes an algorithm for recognizing named entities (symptoms and medications) in medical texts in the Uzbek language, which is considered a low-resource language. The proposed algorithm begins its work by segmenting the text into sentences and word forms, after which each word from the source text is compared with a medical dictionary. Undetected words are subjected to morphological analysis and compared with a dictionary of word roots. The proposed approach not only speeds up the recognition of medical objects, but also minimizes redundancy and ensures data integrity. By integrating traditional linguistic methodologies with computational methods, this research offers a robust solution for efficient recognition of medical named entities in languages with limited available resources.

AB - In the dynamically developing field of computational linguistics, problems associated with the processing of low-resource languages can face to certain difficulties. Moreover, solving such a problem becomes more complicated in the context of medical text processing, where the algorithm is required to do more subtle work than simply understand the context of the source text. The article proposes an algorithm for recognizing named entities (symptoms and medications) in medical texts in the Uzbek language, which is considered a low-resource language. The proposed algorithm begins its work by segmenting the text into sentences and word forms, after which each word from the source text is compared with a medical dictionary. Undetected words are subjected to morphological analysis and compared with a dictionary of word roots. The proposed approach not only speeds up the recognition of medical objects, but also minimizes redundancy and ensures data integrity. By integrating traditional linguistic methodologies with computational methods, this research offers a robust solution for efficient recognition of medical named entities in languages with limited available resources.

KW - Computational Linguistics

KW - Dictionary-Based Extraction

KW - Low-Resource Languages

KW - Medical Entity Recognition

KW - Medical Informatics

KW - Medical Text Processing

KW - Morphological Analysis

KW - Named Entity Recognition

KW - Stemming

KW - Uzbek Language

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85180369041&origin=inward&txGid=9ca32ec2ab037bc4a1e460f0a1041971

UR - https://www.mendeley.com/catalogue/ca5484dc-9ec4-3fb4-a0bd-a4cce7dce75a/

U2 - 10.1109/CSGB60362.2023.10329819

DO - 10.1109/CSGB60362.2023.10329819

M3 - Conference contribution

SN - 9798350307979

T3 - 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings

SP - 85

EP - 89

BT - 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine

Y2 - 28 September 2023 through 29 September 2023

ER -