Cultural keywords in World Englishes: A GloWbE-based study

Collins, Peter

doi:10.2478/icame-2021-0001

Cited by 3 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our current analyses focus on individual words in individual languages, but could be extended in two directions to allow for hierarchies over words and languages. The first extension would consider entire semantic domains instead of individual words (Collins, 2021; Rayson, 2008; Šipka, 2019; Thompson et al., 2020). For example, in addition to asking whether an individual Malay emotion word (e.g., malu ) is used more often than corresponding words in other languages, one could ask whether the combined usage frequency across the entire set of emotion words is greater for Malay than for other languages.…”

Section: Discussionmentioning

confidence: 99%

“…1. We draw on extensive prior work by computational linguists and others who have developed methods for identifying statistical keywords that are associated more strongly with one corpus than another (Collins, 2021; Garimella, Mihalcea, & Pennebaker, 2016; Peirsman, Geeraerts, & Speelman, 2010; Rayson & Chapelle, 2019; Speelman, Grondelaers, & Geeraerts, 2008). Typically, these methods are applied within a single language to study variation across varieties of the language.…”

Section: A Statistical Measure Of Cultural Importancementioning

confidence: 99%

See 1 more Smart Citation

A Computational Approach to Identifying Cultural Keywords Across Languages

Lim,

Stuart,

De Deyne

et al. 2024

Cognitive Science

View full text Add to dashboard Cite

Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian, hati (heart) in Indonesian and Malay, and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful linguistic analyses (Peeters, 2020b; Wierzbicka, 1990). Because keywords are expected to have different statistical properties than related words in other languages, we argue that a quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim, we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally specific words that range from “obvious” examples, such as Amsterdam in Dutch, to non‐obvious yet independently proposed examples, such as hati (heart) in Indonesian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: A Statistical Measure Of Cultural Importancementioning

confidence: 99%

A Computational Approach to Identifying Cultural Keywords Across Languages

Lim,

Stuart,

De Deyne

et al. 2024

Cognitive Science

View full text Add to dashboard Cite

show abstract

“…Our computational approach attempts to formalize the intuitions that lie behind crosslinguistic comparisons of usage frequencies such as those in Figure 1. We draw on extensive prior work by computational linguists and others who have developed methods for identifying words that are associated more strongly with one corpus than another (Speelman, Grondelaers, & Geeraerts, 2008;Peirsman, Geeraerts, & Speelman, 2010;Garimella, Mihalcea, & Pennebaker, 2016;Rayson & Chapelle, 2019;Collins, 2021). Typically, these methods are applied within a single language to study variation across varieties of the language.…”

Section: A Statistical Measure Of Cultural Importancementioning

confidence: 99%

“…Our current analyses focus on individual words in individual languages, but could be extended in two directions to allow for hierarchical structure. The first extension would consider entire semantic domains instead of individual words (Rayson, 2008;Šipka, 2019;Collins, 2021). For example, in addition to asking whether an individual Malay emotion word (e.g.…”

Section: Future Directionsmentioning

confidence: 99%

A Computational Approach to Identifying Cultural Keywords across Languages

Lim¹,

Stuart²,

Deyne³

et al. 2022

Preprint

View full text Add to dashboard Cite

Distinctive aspects of a culture are often reflected in the meaning and usage of words in the language spoken by bearers of that culture. Keywords such as душа (soul) in Russian and gezellig (convivial/cosy/fun) in Dutch are held to be especially culturally revealing, and scholars have identified a number of such keywords using careful lin- guistic analyses (Wierzbicka, 1990; Peeters, 2020b). Because keywords are expected to have different statistical properties than related words in other languages, we argue that quantitative comparison of word usage across languages can help to identify cultural keywords. To support this claim we describe a computational method that compares word frequencies across languages, and apply it to both linguistic corpora and word association data. The method identifies culturally-specific words that range from “obvi- ous” examples such as Amsterdam in Dutch to non-obvious yet independently proposed examples such as душа (soul) in Russian. We show in addition that linguistic corpora and word association data provide converging evidence about culturally-specific words. Our results therefore show how computational analyses and behavioral experiments can supplement the methods previously used by linguists to identify culturally salient words across languages.

show abstract

Frequency, Dispersion, Association, and Keyness

Gries

2024

Studies in Corpus Linguistics

View full text Add to dashboard Cite

This book is an attempt to revisit the main specifically corpus-linguistic statistics/measures the field has been relying on for decades: frequency, dispersion, association, and keyness. The book first discusses the purpose of these measures and how they have been measured. Then, the book makes three main proposals: First, that many measures of dispersion, association, and keyness are too confounded with frequency and how to 'take frequency out of them' to obtain conceptually cleaner and more interpretable measures. Second, that many existing measures can be replaced by the simple information-theoretic measure of the Kullback-Leibler divergence and that it, too, can have frequency 'removed' from it. Third, that corpus linguistics should abandon the tradition of trying to describe its findings with a single number and adopt a tupleization approach instead, where we use several separate dimensions of information for description and interpretation. The book is written in an informal, hands-on style and comes with its own R package featuring functions, example data, and several thousand lines of code exemplifying all applications.

show abstract

Cultural keywords in World Englishes: A GloWbE-based study

Cited by 3 publications

References 29 publications

A Computational Approach to Identifying Cultural Keywords Across Languages

A Computational Approach to Identifying Cultural Keywords Across Languages

A Computational Approach to Identifying Cultural Keywords across Languages

Frequency, Dispersion, Association, and Keyness

Contact Info

Product

Resources

About