Evidence map›Paper›PMID 34151254›Full record

ArticleFrontiers in artificial intelligence2021

Linking Linguistic and Geographic Distance in Four Semantic Domains: Computational Geo-Analyses of Internal and External Factors in a Dialect Continuum.

John L A Huisman, Karlien Franco, Roeland van Hout

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

John L A HuismanCentre for Language Studies, Radboud University, Nijmegen, Netherlands.
Karlien FrancoQLVL, Department of Linguistics, KU Leuven, Leuven, Belgium.
Roeland van HoutCentre for Language Studies, Radboud University, Nijmegen, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dialectometry studies patterns of linguistic variation through correlations between geographic and aggregate measures of linguistic distance. However, aggregating smooths out the role of semantic characteristics, which have been shown to affect the distribution of lexical variants across dialects. Furthermore, although dialectologists have always been well-aware of other variables like population size, isolation and socio-demographic features, these characteristics are generally only included in dialectometric analyses afterwards for further interpretation of the results rather than as explanatory variables. This study showcases linear mixed-effects modelling as a method that is able to incorporate both language-external and language-internal factors as explanatory variables of linguistic variation in the Limburgish dialect continuum in Belgium and the Netherlands. Covering four semantic domains that vary in their degree of basic vs. cultural vocabulary and their degree of standardization, the study models linguistic distances using a combination of external (e.g., geographic distance, separation by water, population size) and internal (semantic density, salience) sources of variation. The results show that both external and internal factors contribute to variation, but that the exact role of each individual factor differs across semantic domains. These findings highlight the need to incorporate language-internal factors in studies on variation, as well as a need for more comprehensive analysis tools to help better understand its patterns.

Indexed as

computational sociolinguisticsdialectometrylexical variationlimburgmixed-effects regressionsemantic variationspatial analysis

Identifiers

PMID34151254
PMCPMC8211982

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