@inproceedings{nguyen-etal-2026-supachoke,
title = "Supachoke at {A}bjad{M}ed: Enhancing {A}rabic Medical Text Classification Using Fine-Tuned {A}ra{BERT}",
author = "Nguyen, Thanh Phu and
Cu, Tuan Thai Huy Nguyen and
Pham, Son Thai and
Nguyen, Tri Duy Ho",
booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.abjadnlp-1.18/",
pages = "127--131",
abstract = "Medical text classification is an important task in healthcare NLP, yet Arabic medical texts remain underexplored due to linguistic complexity and limited annotated data. In this paper, we study the effectiveness of AraBERT, a pre-trained Arabic transformer model, for Arabic medical text classification. We fine-tune AraBERT on a labeled medical dataset and evaluate its performance using standard classification metrics. Experimental results show that our fine-tuned AraBERT model achieves a private leaderboard score of 0.4076 and ranks 13th among participating teams, outperforming classical machine learning baselines and other transformer variants. These findings highlight the potential of transformer-based approaches for Arabic medical NLP and motivate further research."
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<abstract>Medical text classification is an important task in healthcare NLP, yet Arabic medical texts remain underexplored due to linguistic complexity and limited annotated data. In this paper, we study the effectiveness of AraBERT, a pre-trained Arabic transformer model, for Arabic medical text classification. We fine-tune AraBERT on a labeled medical dataset and evaluate its performance using standard classification metrics. Experimental results show that our fine-tuned AraBERT model achieves a private leaderboard score of 0.4076 and ranks 13th among participating teams, outperforming classical machine learning baselines and other transformer variants. These findings highlight the potential of transformer-based approaches for Arabic medical NLP and motivate further research.</abstract>
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%0 Conference Proceedings
%T Supachoke at AbjadMed: Enhancing Arabic Medical Text Classification Using Fine-Tuned AraBERT
%A Nguyen, Thanh Phu
%A Cu, Tuan Thai Huy Nguyen
%A Pham, Son Thai
%A Nguyen, Tri Duy Ho
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F nguyen-etal-2026-supachoke
%X Medical text classification is an important task in healthcare NLP, yet Arabic medical texts remain underexplored due to linguistic complexity and limited annotated data. In this paper, we study the effectiveness of AraBERT, a pre-trained Arabic transformer model, for Arabic medical text classification. We fine-tune AraBERT on a labeled medical dataset and evaluate its performance using standard classification metrics. Experimental results show that our fine-tuned AraBERT model achieves a private leaderboard score of 0.4076 and ranks 13th among participating teams, outperforming classical machine learning baselines and other transformer variants. These findings highlight the potential of transformer-based approaches for Arabic medical NLP and motivate further research.
%U https://aclanthology.org/2026.abjadnlp-1.18/
%P 127-131
Markdown (Informal)
[Supachoke at AbjadMed: Enhancing Arabic Medical Text Classification Using Fine-Tuned AraBERT](https://aclanthology.org/2026.abjadnlp-1.18/) (Nguyen et al., AbjadNLP 2026)
ACL