Predicting natural language descriptions of mono-molecular odorants.

ABSTRACT: There has been recent progress in predicting whether common verbal descriptors such as "fishy", "floral" or "fruity" apply to the smell of odorous molecules. However, accurate predictions have been achieved only for a small number of descriptors. Here, we show that applying natural-language semantic representations on a small set of general olfactory perceptual descriptors allows for the accurate inference of perceptual ratings for mono-molecular odorants over a large and potentially arbitrary set of descriptors. This is noteworthy given that the prevailing view is that humans' capacity to identify or characterize odors by name is poor. We successfully apply our semantics-based approach to predict perceptual ratings with an accuracy higher than 0.5 for up to 70 olfactory perceptual descrip

SUBMITTER: Gutierrez ED 

PROVIDER: S-EPMC6255800 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature