@inproceedings{islam-etal-2025-nbf,
    title = "{NBF} at {S}em{E}val-2025 Task 5: Light-Burst Attention Enhanced System for Multilingual Subject Recommendation",
    author = "Islam, Baharul  and
      Ahmad, Nasim  and
      Barbhuiya, Ferdous  and
      Dey, Kuntal",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.127/",
    pages = "953--958",
    ISBN = "979-8-89176-273-2",
    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."
}