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The application of growth curve modeling for the analysis of diachronic corpora

Bibliographic Data

ID7535112
AuthorsAarno Nini (0000-0003-4218-5130, University of Manchester, corresponding author), Andrea Nini (1 University of Manchester [email protected]), Claudia Corradini (0000-0002-1980-7993, Aston University, corresponding author), Carlo Corradini (0000-0001-9164-4108, 2 Aston University [email protected]), Diansheng Guo (0000-0003-3483-4153, University of South Carolina, corresponding author), J Grieve (0000-0003-3630-7349, Aston University, corresponding author)
Year2017
Volume7
Issue1
Pages102-125
Publication date2017-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueLanguage Dynamics and Change (JOURNAL)
Journal identifiersISSN: 2210-5824 • E-ISSN: 2210-5832
PublisherBrill (PUBLISHER • NL)
DOI10.1163/22105832-00701001
OpenAlexW2606837609
LanguageEN
Citations received1
References cited16

This paper introduces growth curve modeling for the analysis of language change in corpus linguistics. In addition to describing growth curve modeling, which is a regression-based method for studying the dynamics of a set of variables measured over time, we demonstrate the technique through an analysis of the relative frequencies of words that are increasing or decreasing over time in a multi-billion word diachronic corpus of Twitter. This analysis finds that increasing words tend to follow a trajectory similar to the s-curve of language change, whereas decreasing words tend to follow a decelerated trajectory, thereby showing how growth curve modeling can be used to uncover and describe underlying patterns of language change in diachronic corpora

Corpus linguistics · Dynamics (music · Growth curve (statistics · Language change · Latent growth modeling · Linguistics · Natural language processing · Parallel corpora · Regression analysis · Set (abstract data type · Sociology · Statistics · Trajectory · Word (group theory · Complex Network Analysis Techniques · Computer Science · Language and cultural evolution · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence

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Unique citing works1
Citations per year0,33
Citation span2023 - 2023 (1)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 1

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