@inproceedings{maruyama-etal-2025-bridging,
    title = "Bridging Perceptual Gaps in Food {NLP}: A Structured Approach Using Sensory Anchors",
    author = "Maruyama, Kana  and
      Hwang, Angel Hsing-Chi  and
      Besold, Tarek R.",
    editor = "Atwell, Katherine  and
      Biester, Laura  and
      Borah, Angana  and
      Dementieva, Daryna  and
      Ignat, Oana  and
      Kotonya, Neema  and
      Liu, Ziyi  and
      Wan, Ruyuan  and
      Wilson, Steven  and
      Zhao, Jieyu",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.nlp4pi-1.8/",
    pages = "86--98",
    ISBN = "978-1-959429-19-7",
    abstract = "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 free-form 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."
}