Unraveling the Fragrant Language of Odors

Published on November 5, 2022

Imagine trying to describe a smell without using preselected words or relying on other people’s ratings. That’s what researchers are doing as they dive into the world of odor vocabulary in English. By analyzing natural texts, they have discovered 243 words that are strongly associated with olfaction. They then went a step further, grouping these words into four clusters: Offensive, Malodorous, Fragrant, and Edible. These clusters are positioned along a semantic space that primarily differentiates the descriptors based on their pleasantness and edibility. Interestingly, this semantic organization mirrors a similar space derived from perceptual data, suggesting a link between how we perceive smells and how we describe them. This research opens up new possibilities for studying olfaction, a sensory system that is notoriously hard to put into words.

Abstract
The vocabulary for describing odors in English natural language is not well understood, as prior studies of odor descriptions have often relied on preselected descriptors and odor ratings. Here, we present a data-driven approach that automatically identifies English odor descriptors based on their degree of olfactory association, and derive their semantic organization from their distributions in natural texts, using a distributional-semantic language model. We identify 243 descriptors that are much more strongly associated with olfaction than English words in general. We then derive the semantic organization of these olfactory descriptors, and find that it is captured by four clusters that we name Offensive, Malodorous, Fragrant, and Edible. The semantic space derived from our model primarily differentiates descriptors in terms of pleasantness and edibility along which our four clusters are positioned, and is similar to a space derived from perceptual data. The semantic organization of odor vocabulary can thus be mapped using natural language data (e.g., online text), without the limitations of odor-perceptual data and preselected descriptors. Our method may thus facilitate research on olfaction, a sensory system known to often elude verbal description.

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