Targeted minority-class augmentation with BERT-based contextual word insertion improves fine-grained food hazard classification for BERT by about 6%, but gains are inconsistent across techniques and categories.
Ta- ble 11 shows some sample titles and text from the dataset along with their annotated classes
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BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard Classification
Targeted minority-class augmentation with BERT-based contextual word insertion improves fine-grained food hazard classification for BERT by about 6%, but gains are inconsistent across techniques and categories.