Sökning: onr:"swepub:oai:DiVA.org:su-185241" >
The semantic organi...
The semantic organization of the English odor vocabulary
-
- Hörberg, Thomas, 1979- (författare)
- Stockholms universitet,Perception och psykofysik,Avdelningen för allmän språkvetenskap
-
- Olofsson, Jonas K., 1978- (författare)
- Stockholms universitet,Perception och psykofysik
-
(creator_code:org_t)
- 2019
- 2019
- Engelska.
- Relaterad länk:
-
https://su.diva-port... (primary) (Raw object)
-
visa fler...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- Most people find it difficult to name familiar odors (e.g. Herz & Engen, 1996; Jönsson & Stevenson, 2014). Most languages, including English, lack a vocabulary that is devoted to describing odor qualities (as compared to, e.g., a color term vocabulary). Across languages, olfaction has been shown to be the sense with the poorest linguistic codability (i.e. naming consistency, see e.g. Majid et al., 2018). Instead of using devoted, abstract terms for describing odors, speakers of many languages often resort to source-based (e.g. ‘citrusy’) odor descriptions, and relatively little is still known about the vocabulary that is used to describe odors. Attempts to establish “primary odor descriptors” have been unsuccessful in describing wider varieties of odor qualities, and no standard has been agreed upon (e.g. Kaeppler & Mueller, 2013).To date, research on odor vocabulary has rarely been done from a data-driven, empirical perspective.We present a study on the semantic organization of the odor vocabulary, based on the distribution of words in olfactory and gustatory contexts, using a three-billion-word corpus of written English. Using a data-driven, computational linguistic approach recently developed in our lab (Iatropoulos et al., 2018), we quantify terms with respect to degree of olfactory-semantic content they convey. We then derive the semantic organization of the top 200 olfactory-related terms, using a distributional-semantic word vector model, which represents semantic distances as vector distances in a multidimensional space. In order to capture olfactory and gustatory word senses, the model is trained on olfactory and gustatory contexts, using the word2vec neural network implementation (Mikolov, Chen, Corrado, & Dean, 2013). Based on the semantic distances, we then use dimensionality reduction and clustering techniques (i.e., PCA and hierarchical clustering) to derive a 3-dimensional, corpus-based semantic space of the descriptors, and six principal descriptor clusters.Using descriptor distances based on the Draveneiks odor-term rating data set (Dravnieks, 1992), we also derive a semantic space with six specific clusters for the Draveneiks terms. The organization and clustering of our corpus-based semantic space matches with the ratings-based semantic space, thereby showing the viability of our corpus-based approach. Based on our analyses of the corpus-based data, we finally propose a novel domain-general odor term taxonomy (i.e., a domain-general odor wheel) that captures the dimensions and clusters identified in our analyses.Dravnieks, A. (1992). Atlas of odor character profiles. Philadelphia, PA, USA: American Society for Testing and Materials.Herz, R. S., & Engen, T. (1996). Odor memory: Review and analysis. Psychonomic Bulletin & Review, 3(3), 300–313.Iatropoulos, G., Herman, P., Lansner, A., Karlgren, J., Larsson, M., & Olofsson, J. K. (2018). The language of smell: Connecting linguistic and psychophysical properties of odor descriptors. Cognition, 178, 37–49.Jönsson, F. U., & Stevenson, R. J. (2014). Odor Knowledge, Odor Naming, and the “Tip-of-the-Nose” Experience. I B. L. Schwartz & A. S. Brown (Red.), Tip-of-the-Tongue States and Related Phenomena (s. 305–326).Kaeppler, K., & Mueller, F. (2013). Odor Classification: A Review of Factors Influencing Perception-Based Odor Arrangements. Chemical Senses, 38(3), 189–209.Majid, A., Roberts, S. G., Cilissen, L., Emmorey, K., Nicodemus, B., O’Grady, L., … Levinson, S. C. (2018). Differential coding of perception in the world’s languages. Proceedings of the National Academy of Sciences, 115(45), 11369–11376.Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781 [cs].
Ämnesord
- HUMANIORA -- Språk och litteratur -- Jämförande språkvetenskap och allmän lingvistik (hsv//swe)
- HUMANITIES -- Languages and Literature -- General Language Studies and Linguistics (hsv//eng)
- SAMHÄLLSVETENSKAP -- Psykologi -- Psykologi (hsv//swe)
- SOCIAL SCIENCES -- Psychology -- Psychology (hsv//eng)
Nyckelord
- olfaction
- olfactory semantics
- corpus linguistics
- word embeddings
- General Linguistics
- allmän språkvetenskap
- Psychology
- psykologi
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)