Cargando…
Predicting natural language descriptions of mono-molecular odorants
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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255800/ https://www.ncbi.nlm.nih.gov/pubmed/30478272 http://dx.doi.org/10.1038/s41467-018-07439-9 |
_version_ | 1783374019775430656 |
---|---|
author | Gutiérrez, E. Darío Dhurandhar, Amit Keller, Andreas Meyer, Pablo Cecchi, Guillermo A. |
author_facet | Gutiérrez, E. Darío Dhurandhar, Amit Keller, Andreas Meyer, Pablo Cecchi, Guillermo A. |
author_sort | Gutiérrez, E. Darío |
collection | PubMed |
description | 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 descriptors, a ten-fold increase in the number of descriptors from previous attempts. These results imply that the semantic distance between descriptors defines the equivalent of an odorwheel. |
format | Online Article Text |
id | pubmed-6255800 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-62558002018-11-28 Predicting natural language descriptions of mono-molecular odorants Gutiérrez, E. Darío Dhurandhar, Amit Keller, Andreas Meyer, Pablo Cecchi, Guillermo A. Nat Commun Article 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 descriptors, a ten-fold increase in the number of descriptors from previous attempts. These results imply that the semantic distance between descriptors defines the equivalent of an odorwheel. Nature Publishing Group UK 2018-11-26 /pmc/articles/PMC6255800/ /pubmed/30478272 http://dx.doi.org/10.1038/s41467-018-07439-9 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Gutiérrez, E. Darío Dhurandhar, Amit Keller, Andreas Meyer, Pablo Cecchi, Guillermo A. Predicting natural language descriptions of mono-molecular odorants |
title | Predicting natural language descriptions of mono-molecular odorants |
title_full | Predicting natural language descriptions of mono-molecular odorants |
title_fullStr | Predicting natural language descriptions of mono-molecular odorants |
title_full_unstemmed | Predicting natural language descriptions of mono-molecular odorants |
title_short | Predicting natural language descriptions of mono-molecular odorants |
title_sort | predicting natural language descriptions of mono-molecular odorants |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255800/ https://www.ncbi.nlm.nih.gov/pubmed/30478272 http://dx.doi.org/10.1038/s41467-018-07439-9 |
work_keys_str_mv | AT gutierrezedario predictingnaturallanguagedescriptionsofmonomolecularodorants AT dhurandharamit predictingnaturallanguagedescriptionsofmonomolecularodorants AT kellerandreas predictingnaturallanguagedescriptionsofmonomolecularodorants AT meyerpablo predictingnaturallanguagedescriptionsofmonomolecularodorants AT cecchiguillermoa predictingnaturallanguagedescriptionsofmonomolecularodorants |