UniBuc at SemEval-2025 Task 9: Similarity Approaches to Classification

AbstractIn this paper, we present a similarity-based method for explainable classification in the context of the SemEval 2025 Task 9: The Food Hazard Detection Challenge. Our proposed system is essentially unsupervised, leveraging the semantic properties of the labels. This approach brings some key advantages over typical classification systems. First, similarity metrics offer a more intuitive interpretation. Next, this technique allows for inference on novel labels. Finally, there is a non-negligible amount of ambiguous labels, so learning a direct mapping does not lead to meaningful representations.Our team ranks 13th for the second sub-task among participants that used only the title and the text as features. Our method is generic and can be applied to any classification task.