@inproceedings{papadopoulou-etal-2025-brightcookies,
    title = "{B}right{C}ookies at {S}em{E}val-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification",
    author = {Papadopoulou, Foteini  and
      Mutlu, Osman  and
      {\"O}zen, Neris  and
      Van Der Velden, Bas  and
      Hendrickx, Iris  and
      Hurriyetoglu, Ali},
    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-1.124/",
    pages = "914--930",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our system developed for the SemEval-2025 Task 9: The Food Hazard Detection Challenge. The shared task{'}s objective is to evaluate explainable classification systems for classifying hazards and products in two levels of granularity from web-collected food recall incident reports. In this work, we propose text augmentation techniques as a way to improve poor performance in minority classes and compare their effect for each category on various transformer and machine learning models. We apply three word-level data augmentation techniques, namely synonym replacement, random word swapping, and contextual word insertion utilizing BERT. The resultsshow that transformer models tend to have a better overall performance. Meanwhile, a statistically significant improvement (P 0.05) was observed in the fine-grained categories when using BERT to compare the baseline model with the three augmented models, which achieved a 6{\%} increase in correct predictions for minority hazard classes. This suggests that targeted augmentation of minority classes can improve the performance of transformer models."
}