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Mapping the semantic organization of the English odor vocabulary using natural language data

Olfactory experiences are hard to verbalize, partly because most languages lack devoted odor vocabularies. Yet, there is a need for a standardized odor vocabulary, but no descriptive system for describing the full range of odor experiences has been agreed upon. Many studies of the English odor vocabulary have been based on perceptual data such as odor-descriptor ratings, thereby being limited to a small set of pre-selected descriptors. In the present study, we present a data-driven approach that automatically identifies odor descriptors in English, and then derive their semantic organization on the basis of their distributions in natural texts. Olfactory descriptors are automatically identified on the basis of their degree of olfactory association, and their semantic organization is derived with a distributional-semantic word embedding model. We identify and derive the semantic organization of the descriptors most frequently used to describe odors and flavors in English, both within and across source-based, abstract and evaluative descriptor categories. Our method is to a large extent able to capture semantic differences between descriptors related to aroma and flavor qualities, rather than e.g. functional or linguistic aspects, in that it primarily differentiates descriptors with respect to valence and edibility, and the semantic space derived from it is qualitatively similar to a space derived from perceptual data.