@inproceedings{sivanaiah-etal-2025-techssn3,
    title = "{T}ech{SSN}3 at {S}em{E}val-2025 Task 9: Food Hazard and Product Detection - Category Identification and Vector Prediction",
    author = "Sivanaiah, Rajalakshmi  and
      S, Karpagavalli  and
      S, Karthikeyan  and
      C, Krithika",
    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-1.67/",
    pages = "482--486",
    ISBN = "979-8-89176-273-2",
    abstract = "Food safety is a critical global concern, and timely detection of food-related hazards is essential for public health and economic stability. The automated detection of food hazards from textual data can enhance food safety monitoring by enabling early identification of potential risks. In the Food Hazard Detection task, we address two key challenges: (ST1) food hazard-category and product-category classification and (ST2) food hazard and product vector detection. For ST1, we employ BertForSequenceClassification, leveraging its powerful contextual understanding for accurate food hazard classification. For ST2, we utilize a Random Forest Classifier, which effectively captures patterns in the extracted features for food hazard and product vector detection. This paper presents the results of the TechSSN3 team at SemEval-2025 Food Hazard Detection Task ."
}