Using Multilingual Bidirectional Encoder Representations from Transformers on Medical Corpus for Kurdish Text Classification

Badawi, Soran S. (2023) Using Multilingual Bidirectional Encoder Representations from Transformers on Medical Corpus for Kurdish Text Classification. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 11 (1). pp. 10-15. ISSN 2410-9355

Text (Research Article) ARO.11088-VOL11.NO1.2023.ISSUE20-PP10-15.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (1MB)

Abstract

Technology has dominated a huge part of human life. Furthermore, technology users use language continuously to express feelings and sentiments about things. The science behind identifying human attitudes toward a particular product, service,or topic is one of the most active fields of research, and it is called sentiment analysis. While the English language is making real progress in sentiment analysis daily, other less-resourced languages, such as Kurdish, still suffer from fundamental issues and challenges in Natural Language Processing (NLP). This paper experimentswith the recently published medical corpus using the classical machine learning method and the latest deep learning tool in NLP and Bidirectional Encoder Representations from Transformers (BERT). We evaluated the findings of both machine learning and deep learning. The outcome indicates that BERT outperforms all the machine learning classifiers by scoring (92%) in accuracy, which is by two points higher than machine learning classifiers.

Item Type:	Article
Uncontrolled Keywords:	Bidirectional Encoder Representations from Transformers, Deep learning, Machine learning, Natural language processing, Sentiment analysis, Transformers
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	ARO-The Scientific Journal of Koya University > VOL 11, NO 1 (2023)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	02 Feb 2023 06:41
Last Modified:	02 Feb 2023 06:41
URI:	http://eprints.koyauniversity.org/id/eprint/347

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