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        <title>HCMUS_PrompterXPrompter at AbjadMed: When Classification Meets Retrieval: Taming the Long Tail in Arabic Medical Text Classification</title>
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        <namePart type="given">Duy</namePart>
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        <namePart type="given">Ha</namePart>
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    <abstract>Medical text classification is high-stakes work, yet models often falter precisely where they are needed most: on rare, critical conditions buried in the long tail of the data distribution. In the EACL 2026 ABJAD-NLP Shared Task, we confronted this challenge with a dataset of Arabic medical questions heavily skewed towards a few common topics, leaving dozens of categories with fewer than ten examples. We present HybridMed, a system that effectively tames this long tail by marrying the semantic generalization of a fine-tuned Arabic BERT model with the precise, instance-based memory of k-nearest neighbor retrieval. This complementary union allowed our system to achieve a macro-F1 score of 0.4902, demonstrating that for diverse and imbalanced medical data, the whole is indeed greater than the sum of its parts.</abstract>
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%0 Conference Proceedings
%T HCMUS_PrompterXPrompter at AbjadMed: When Classification Meets Retrieval: Taming the Long Tail in Arabic Medical Text Classification
%A Dao Sy, Duy Minh
%A Huynh, Trung Kiet
%A Duong, Nguyen Dinh Ha
%A Tran, Nguyen Chi
%A Nguyen Lam, Phu Quy
%A Pham Phu, Hoa
%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 dao-sy-etal-2026-hcmus
%X Medical text classification is high-stakes work, yet models often falter precisely where they are needed most: on rare, critical conditions buried in the long tail of the data distribution. In the EACL 2026 ABJAD-NLP Shared Task, we confronted this challenge with a dataset of Arabic medical questions heavily skewed towards a few common topics, leaving dozens of categories with fewer than ten examples. We present HybridMed, a system that effectively tames this long tail by marrying the semantic generalization of a fine-tuned Arabic BERT model with the precise, instance-based memory of k-nearest neighbor retrieval. This complementary union allowed our system to achieve a macro-F1 score of 0.4902, demonstrating that for diverse and imbalanced medical data, the whole is indeed greater than the sum of its parts.
%U https://aclanthology.org/2026.abjadnlp-1.7/
%P 55-59

Markdown (Informal)

[HCMUS_PrompterXPrompter at AbjadMed: When Classification Meets Retrieval: Taming the Long Tail in Arabic Medical Text Classification](https://aclanthology.org/2026.abjadnlp-1.7/) (Dao Sy et al., AbjadNLP 2026)

• HCMUS_PrompterXPrompter at AbjadMed: When Classification Meets Retrieval: Taming the Long Tail in Arabic Medical Text Classification (Dao Sy et al., AbjadNLP 2026)

ACL

• Duy Minh Dao Sy, Trung Kiet Huynh, Nguyen Dinh Ha Duong, Nguyen Chi Tran, Phu Quy Nguyen Lam, and Hoa Pham Phu. 2026. HCMUS_PrompterXPrompter at AbjadMed: When Classification Meets Retrieval: Taming the Long Tail in Arabic Medical Text Classification. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 55–59, Rabat, Morocco. Association for Computational Linguistics.