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Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes

Inspired by work in comparative sociolinguistics and quantitative dialectometry, we sketch a corpus-based method (Variation-Based Distance & Similarity Modeling—VADIS for short) to rigorously quantify the similarity between varieties and dialects as a function of the correspondence of the ways i...

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Autores principales: Szmrecsanyi, Benedikt, Grafmiller, Jason, Rosseel, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861267/
https://www.ncbi.nlm.nih.gov/pubmed/33733112
http://dx.doi.org/10.3389/frai.2019.00023
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author Szmrecsanyi, Benedikt
Grafmiller, Jason
Rosseel, Laura
author_facet Szmrecsanyi, Benedikt
Grafmiller, Jason
Rosseel, Laura
author_sort Szmrecsanyi, Benedikt
collection PubMed
description Inspired by work in comparative sociolinguistics and quantitative dialectometry, we sketch a corpus-based method (Variation-Based Distance & Similarity Modeling—VADIS for short) to rigorously quantify the similarity between varieties and dialects as a function of the correspondence of the ways in which language users choose between different ways of saying the same thing. To showcase the potential of the method, we present a case study that investigates three syntactic alternations in some nine international varieties of English. Key findings include that (a) probabilistic grammars are remarkably similar and stable across the varieties under study; (b) in many cases we see a cluster of “native” (a.k.a. Inner Circle) varieties, such as British English, whereas “non-native” (a.k.a. Outer Circle) varieties, such as Indian English, are a more heterogeneous group; and (c) coherence across alternations is less than perfect.
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spelling pubmed-78612672021-03-16 Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes Szmrecsanyi, Benedikt Grafmiller, Jason Rosseel, Laura Front Artif Intell Artificial Intelligence Inspired by work in comparative sociolinguistics and quantitative dialectometry, we sketch a corpus-based method (Variation-Based Distance & Similarity Modeling—VADIS for short) to rigorously quantify the similarity between varieties and dialects as a function of the correspondence of the ways in which language users choose between different ways of saying the same thing. To showcase the potential of the method, we present a case study that investigates three syntactic alternations in some nine international varieties of English. Key findings include that (a) probabilistic grammars are remarkably similar and stable across the varieties under study; (b) in many cases we see a cluster of “native” (a.k.a. Inner Circle) varieties, such as British English, whereas “non-native” (a.k.a. Outer Circle) varieties, such as Indian English, are a more heterogeneous group; and (c) coherence across alternations is less than perfect. Frontiers Media S.A. 2019-11-05 /pmc/articles/PMC7861267/ /pubmed/33733112 http://dx.doi.org/10.3389/frai.2019.00023 Text en Copyright © 2019 Szmrecsanyi, Grafmiller and Rosseel. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Szmrecsanyi, Benedikt
Grafmiller, Jason
Rosseel, Laura
Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes
title Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes
title_full Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes
title_fullStr Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes
title_full_unstemmed Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes
title_short Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes
title_sort variation-based distance and similarity modeling: a case study in world englishes
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861267/
https://www.ncbi.nlm.nih.gov/pubmed/33733112
http://dx.doi.org/10.3389/frai.2019.00023
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