Representations of Language Varieties Are Reliable Given Corpus Similarity Measures

Jonathan Dunn


Abstract
This paper measures similarity both within and between 84 language varieties across nine languages. These corpora are drawn from digital sources (the web and tweets), allowing us to evaluate whether such geo-referenced corpora are reliable for modelling linguistic variation. The basic idea is that, if each source adequately represents a single underlying language variety, then the similarity between these sources should be stable across all languages and countries. The paper shows that there is a consistent agreement between these sources using frequency-based corpus similarity measures. This provides further evidence that digital geo-referenced corpora consistently represent local language varieties.
Anthology ID:
2021.vardial-1.4
Volume:
Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects
Month:
April
Year:
2021
Address:
Kiyv, Ukraine
Venues:
EACL | VarDial
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–38
Language:
URL:
https://aclanthology.org/2021.vardial-1.4
DOI:
Bibkey:
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PDF:
https://aclanthology.org/2021.vardial-1.4.pdf