FEAST: Retrieval-Augmented Multi-Hierarchical Food Classification for the FoodEx2 System
Hierarchical text classification (HTC) and extreme multi-label classification (XML) tasks face compounded challenges from complex label interdependencies, data sparsity, and extreme output dimensions. These challenges are exemplified in the European Food Safety Authority's FoodEx2 system-a standardized food classification framework essential for food consumption monitoring and contaminant exposure assessment across Europe. FoodEx2 coding transforms natural language food descriptions into a set of codes from multiple standardized hierarchies, but faces implementation barriers due to its complex structure. Given a food description (e.g., "organic yogurt''), the system identifies its base te...
| Authors | Lorenzo Molfetta, Alessio Cocchieri, Stefano Fantazzini et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
Connected in the knowledge graph
- disi-unibo-nlp/feast-foodex2Dataset · cited by
Models and datasets referencing this paper
- disi-unibo-nlp/feast-foodex2Dataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.