Croijmans, Ilja, Hendrickx, Iris, Lefever, Els et al. (2 more authors) (2020) Uncovering the language of wine experts. Natural language engineering. pp. 511-530. ISSN 1469-8110
Abstract
Talking about odors and flavors is difficult for most people, yet experts appear to be able to convey critical information about wines in their reviews. This seems to be a contradiction, and wine expert descriptions are frequently received with criticism. Here, we propose a method for probing the language of wine reviews, and thus offer a means to enhance current vocabularies, and as a by-product question the general assumption that wine reviews are gibberish. By means of two different quantitative analyses-support vector machines for classification and Termhood analysis-on a corpus of online wine reviews, we tested whether wine reviews are written in a consistent manner, and thus may be considered informative; and whether reviews feature domain-specific language. First, a classification paradigm was trained on wine reviews from one set of authors for which the color, grape variety, and origin of a wine were known, and subsequently tested on data from a new author. This analysis revealed that, regardless of individual differences in vocabulary preferences, color and grape variety were predicted with high accuracy. Second, using Termhood as a measure of how words are used in wine reviews in a domain-specific manner compared to other genres in English, a list of 146 wine-specific terms was uncovered. These words were compared to existing lists of wine vocabulary that are currently used to train experts. Some overlap was observed, but there were also gaps revealed in the extant lists, suggesting these lists could be improved by our automatic analysis.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Funding Information: Acknowledgements. This work was funded by The Netherlands Organization for Scientific Research: NWO VICI grant “Human olfaction at the intersection of language, culture and biology” to A. Majid [grant number 277-70-011]. Thanks to Chris van der Lee for processing the corpus data, and Laura Speed and Artin Arshamian for comments on an earlier draft of the manuscript. We would like to thank three anonymous reviewers for their thoughtful comments and suggestions on two earlier drafts of this manuscript. Publisher Copyright: © Cambridge University Press 2019. This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial licence (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright: Copyright 2020 Elsevier B.V., All rights reserved. |
Keywords: | Corpus linguistics,Information extraction,Semantics,Statistical methods,Wine expertise |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Psychology (York) |
Depositing User: | Pure (York) |
Date Deposited: | 25 Feb 2021 17:20 |
Last Modified: | 17 Dec 2024 00:19 |
Published Version: | https://doi.org/10.1017/S1351324919000500 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1017/S1351324919000500 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:171595 |
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