Bridging Perceptual Gaps in Food NLP: A Structured Approach Using Sensory Anchors

Understanding how humans perceive and describe food is essential for NLP applications such as semantic search, recommendation, and structured food communication.However, textual similarity often fails to reflect perceptual similarity, which is shaped by sensory experience, wine knowledge, and individual context.To address this, we introduce Sensory Anchors-structured reference points that align textual and perceptual representations.Using Red Wine as a case study, we collect freeform descriptions, metaphor-style responses, and perceptual similarity rankings from participants with varying levels of wine knowledge.These rankings reflect holistic perceptual judgments, with wine knowledge emerging as a key factor.Participants with higher wine knowledge produced more consistent rankings and moderately aligned descriptions, while those with lower knowledge showed greater variability.These findings suggest that structured descriptions based on higher wine knowledge may not generalize across users, underscoring the importance of modeling perceptual diversity.We also find that metaphor-style prompts enhance alignment between language and perception, particularly for less knowledgeable participants.Sensory Anchors thus provide a flexible foundation for capturing perceptual variability in food language, supporting the development of more inclusive and interpretable NLP systems.