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1
Measuring Linguistic Diversity During COVID-19
Coupe T; Dunn J; Adams B. - 2020
Abstract: Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of linguistic diversity using restrictions on international travel resulting from the COVID-19 pandemic. Previous work has mapped the distribution of languages using geo-referenced social media and web data. The goal, however, has been to describe these corpora themselves rather than to make inferences about underlying populations. This paper shows that a difference-indifferences method based on the Herfindahl Hirschman Index can identify the bias in digital corpora that is introduced by non-local populations. These methods tell us where significant changes have taken place and whether this leads to increased or decreased diversity. This is an important step in aligning digital corpora like social media with the real-world populations that have produced them.
Keyword: communication and culture::4704 - Linguistics::470403 - Computational linguistics; communication and culture::4704 - Linguistics::470404 - Corpus linguistics; communication and culture::4704 - Linguistics::470411 - Sociolinguistics; Fields of Research::47 - Language
URL: https://hdl.handle.net/10092/101220
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2
What Metaphor Identification Systems Can Tell Us About Metaphor-in-Language
Dunn J. - 2019
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3
Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar
Dunn J. - : Association for Computational Linguistics, 2019
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4
Measuring Metaphoricity
Dunn J. - 2019
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5
Multi-Dimensional Abstractness in Cross-Domain Mappings
Dunn J. - 2019
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6
Modeling the Complexity and Descriptive Adequacy of Construction Grammars
Dunn J. - 2019
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7
Language-Independent Ensemble Approaches to Metaphor Identification
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8
Modeling Global Syntactic Variation in English Using Dialect Classification
Dunn J. - : Association for Computational Linguistics, 2019
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