From meaning to perception - exploring the space between word and odor perception embeddings

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

In this paper we propose the use of the Word2vec algorithm in order to obtain odor perception embeddings (or smell embeddings), only using publicly available perfume descriptions. Besides showing meaningful similarity relationships among each other, these embeddings also demonstrate to possess some shared information with their respective word embeddings. The meaningfulness of these embeddings suggests that aesthetics might provide enough constraints for using algorithms motivated by distributional semantics on non-randomly combined data. Furthermore they provide possibilities for new ways of classifying odors and analyzing perfumes. In an additional tentative experiment we explore the possibility of a mapping between the word embedding space and the odor perception embedding space by fitting a regressor on the shared vocabulary and then predict the odor perception embeddings of words without an a priori associated smell, such as night or sky.
Original languageEnglish
Title of host publicationFirst International Workshop on Multisensory Data & Knowledge : Workshop at LDK 2021
Publication dateSep 2021
Publication statusPublished - Sep 2021

ID: 270622462