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    <abstract>This paper presents a system for automated subject tagging in a bilingual academic setting. Our approach leverages a novel burst attention mechanism to enhance the alignment between article and subject embeddings, derived from a large cross-lingual subject corpus. By employing a margin-based loss with negative sampling, our resource-efficient model achieves competitive performance in both quantitative and qualitative evaluations. Experimental results demonstrate average recall rates of 32.24% on the full test set, along with robust performance on specialized subsets, making our system well-suited for large-scale subject recommendation tasks.</abstract>
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%0 Conference Proceedings
%T NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation
%A Islam, Baharul
%A Ahmad, Nasim
%A Barbhuiya, Ferdous
%A Dey, Kuntal
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F islam-etal-2025-nbf
%X This paper presents a system for automated subject tagging in a bilingual academic setting. Our approach leverages a novel burst attention mechanism to enhance the alignment between article and subject embeddings, derived from a large cross-lingual subject corpus. By employing a margin-based loss with negative sampling, our resource-efficient model achieves competitive performance in both quantitative and qualitative evaluations. Experimental results demonstrate average recall rates of 32.24% on the full test set, along with robust performance on specialized subsets, making our system well-suited for large-scale subject recommendation tasks.
%U https://aclanthology.org/2025.semeval-1.127/
%P 953-958

Markdown (Informal)

[NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation](https://aclanthology.org/2025.semeval-1.127/) (Islam et al., SemEval 2025)

• NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation (Islam et al., SemEval 2025)

ACL

• Baharul Islam, Nasim Ahmad, Ferdous Barbhuiya, and Kuntal Dey. 2025. NBF at SemEval-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 953–958, Vienna, Austria. Association for Computational Linguistics.