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Uncovering the language of wine experts
- Source :
- Natural Language Engineering, Natural Language Engineering, 26(5), 511. Cambridge University Press, Natural Language Engineering, 26, 5, pp. 511-530, Natural Language Engineering, 26, 511-530, Natural Language Engineering, 1-20. Cambridge University Press, STARTPAGE=1;ENDPAGE=20;ISSN=1351-3249;TITLE=Natural Language Engineering, NATURAL LANGUAGE ENGINEERING
- Publication Year :
- 2020
-
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.
- Subjects :
- wine expertise
AROMA
050101 languages & linguistics
Linguistics and Language
Vocabulary
Computer science
media_common.quotation_subject
corpus linguistics
Gibberish
Semantics
computer.software_genre
Languages and Literatures
050105 experimental psychology
Language and Linguistics
Artificial Intelligence
Corpus linguistics
COLOR
0501 psychology and cognitive sciences
information extraction
Set (psychology)
semantics
media_common
Wine
PERCEPTION
SMELL
IDENTIFICATION
business.industry
LT3
05 social sciences
STANDARDIZED SYSTEM
Variety (linguistics)
Language & Communication
Information extraction
DISCRIMINATION
OLD WINE
ODOR
statistical methods
Artificial intelligence
Language & Speech Technology
business
computer
Software
Natural language processing
FLAVOR
Subjects
Details
- Language :
- English
- ISSN :
- 14698110 and 13513249
- Database :
- OpenAIRE
- Journal :
- Natural Language Engineering, Natural Language Engineering, 26(5), 511. Cambridge University Press, Natural Language Engineering, 26, 5, pp. 511-530, Natural Language Engineering, 26, 511-530, Natural Language Engineering, 1-20. Cambridge University Press, STARTPAGE=1;ENDPAGE=20;ISSN=1351-3249;TITLE=Natural Language Engineering, NATURAL LANGUAGE ENGINEERING
- Accession number :
- edsair.doi.dedup.....fa2908c2131aab46536f8a4ca0a3d1d1