@inproceedings{le-etal-2025-anastasia,
    title = "Anastasia at {S}em{E}val-2025 Task 9: Subtask 1, Ensemble Learning with Data Augmentation and Focal Loss for Food Risk Classification.",
    author = "Le, Tung  and
      Ngo, Tri  and
      Dang, Trung",
    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-1.20/",
    pages = "141--147",
    ISBN = "979-8-89176-273-2",
    abstract = "Our approach for the SemEval-2025 Task 9: Subtask 1, The Food Hazard Detection Challenge showcases a robust ensemble learning methodology designed to classify food hazards and associated products from incident report titles. By incorporating advanced data augmentation techniques, we significantly enhanced model generalization and addressed class imbalance through the application of focal loss. This strategic combination led to our team securing the Top 1 position, achieving an impressive score of 0.8223, underscoring the strength of our solution in improving classification performance for food safety risk assessment."
}