Bridging Perceptual Gaps in Food NLP : A Structured Approach Using Sensory Anchors (2025)

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.

## introduction understanding how humans perceive and describe food is essential for developing nlp-driven applications in food analysis. these include structured food descriptions, personalized recommendations, pairing systems, and models that integrate human sensory perception. while traditional nlp approaches often rely on textual similarity @xcite , human food perception is influenced by a combination of sensory experience, domain knowledge, cultural context, and personal preference @xcite . this raises a critical question: can textual similarity alone adequately reflect perceptual similarity in the human experience of food? prior work has explored knowledge-driven representations of food perception, such as expertdefined flavor wheels and structured lexicons @xcite , as well as consumer-generated taxonomies @xcite . however, these frameworks typically assume a fixed vocabulary and are not easily adaptable to users with different knowledge levels or interpretive styles. moreover, most perceptual modeling has focused on specific products (e.g., branded wines), whereas category-level modeling (e.g., red wine) remains underexplored. yet, modeling perception at the category level is essential for building generalizable systems that align with human conceptual organization @xcite and support perception-aware nlp. perceptual framing differs substantially by knowledge level. high-knowledge individuals tend to describe food using structured sensory categories (e.g., black fruits, red fruits, oak), while low-knowledge individuals often rely on more impressionistic, less differentiated expressions @xcite . interestingly, prior research suggests that despite these differences in language, their underlying sensory perceptions may be quite similar @xcite . this implies that the divergence in descriptions arises not from differences in raw sensory sensitivity, but from differences in prior exposure and conceptual organization. as individuals gain more experience, their representations of similarity become increasingly refined-not necessarily because perception itself changes, but because experience reshapes how similarity is conceptualized @xcite . this variability poses a challenge for nlp systems, which must bridge divergent perceptual structures across users @xcite . models based solely on textual similarity may fail to capture meaningful sensory similarity, particularly when they overlook how knowledge shapes both perception and language use @xcite . although prior work in computational gastronomy and sensory science has advanced models of flavor networks @xcite , ingredient pairings @xcite , and multisensory integration @xcite , few efforts have addressed perceptual modeling at the category level or across diverse knowledge levels. while large-scale food nlp datasets have been enabled by crowdsourced annotations @xcite , the subjectivity of perception-especially among heterogeneous users-remains a core obstacle. we respond to this challenge by proposing a flexible framework that can capture diverse sources of perceptual variation-including domain knowledge, sensory experience, and cultural background-without attempting to reduce perceptual judgments to any one factor. as an instantiation of this framework, we introduce sensory anchors-structured reference points designed to align perceptual similarity judgments and textual expressions across user groups. while this study focuses on participants' knowledge level as the analytic lens, the framework itself is general and can accommodate other sources of perceptual variability, such as sensory experience or cultural background, by substituting the grouping axis and comparative analysis accordingly. using red wine as a case study, we collect both free-form descriptions and perceptual similarity rankings from participants with varying levels of wine knowledge. for clarity, we refer to partici-pants with higher or lower wine knowledge scores as "high-knowledge" and "low-knowledge" participants, respectively. we treat the similarity rankings as holistic judgments, potentially shaped by a range of factors including direct sensory experience, conceptual associations, and prior exposure. rather than disentangling these factors, we focus our analysis on how domain knowledge influences the relationship between perception and language. to support participants in articulating nuanced perceptual similarities, we incorporate metaphorstyle prompts that encourage them to frame their judgments using familiar conceptual language. our study makes the following contributions: by bridging the gap between textual and perceptual similarity, this study offers insights that may inform the design of perception-aware nlp systems to support inclusive, interpretable, and user-aligned food communication. such systems could enhance tasks such as search and retrieval, knowledge-sensitive recommendations, and structured description generation. more broadly, our framework may contribute to applications in dietary education, accessibility, and culturally-aware food design-supporting socially relevant goals aligned with the potential of nlp technologies. ## data collection and annotation this section describes the data collection methodology, the selection of sensory anchors, and the annotation process. ## experimental analysis this section investigates how domain knowledge affects both perceptual similarity judgments and sensory descriptions, using sensory anchors as structured reference points. we analyze (1) the quality and content of free-form descriptions, (2) the structure of perceptual similarity rankings and their alignment with textual data, and (3) the implications of these patterns for perception-aware nlp. ## conclusion & future work this study investigated how domain knowledge shapes food perception and description, introducing sensory anchors as structured reference points for modeling perceptual similarity in language. analyses of participants' descriptions and similarity rankings indicate that perceptual structures vary across knowledge levels, and that descriptions from high-knowledge participants may not generalize well to those with lower knowledge. metaphorstyle prompts improved alignment in both groups, highlighting the role of linguistic scaffolding in supporting consistent mappings between perception and language. sensory anchors offer a flexible and interpretable framework for linking textual and perceptual representations in food-related nlp. although this study focused on red wine and used wine knowledge as the primary axis of variation, the framework is not inherently limited to domain knowledge. it can extend to other sources of perceptual variation-such as sensory experience, cultural background, or affective associations. im-portantly, our study deliberately targeted categorylevel rather than instance-level perception. this design allows us to investigate how people conceptualize and describe broad sensory categories (e.g., red wine) based on general experience, which is crucial for building scalable, knowledge-sensitive, and conceptually robust nlp systems. applications include inclusive recommendation and retrieval systems, culturally adaptive food communication, food and beverage pairing support, and personalized sensory education tools-advancing the broader goal of aligning language with perception across diverse user groups. limitations. this study has several limitations. first, the sample size was relatively small (@xmath0.s.-based participants, limiting generalizability and cultural diversity. second, participants were grouped by a median split (@xmath1, which may obscure fine-grained differences near the cutoff. third, while we assessed descriptive quality using structural metrics and human annotation, we did not evaluate semantic accuracy or domain-specific vocabulary usage, which could clarify how meaning varies with knowledge. lastly, our exploratory correlation analyses did not include correction for multiple comparisons, raising the risk of spurious correlations. future work. future research could extend the framework to other food domains and investigate perception across cultural or linguistic groups. incorporating finer group definitions (e.g., percentilebased or continuous modeling) and controlled experimental conditions may help disentangle different sources of perceptual variability. additionally, integrating semantic evaluation techniques could further improve our understanding of how perceptual similarity is reflected in language.