mlsanityData to 7 Oct 2026

Representations of Language Varieties Are Reliable Given Corpus Similarity Measures

arXiv 2104.01294 ↗First posted 3 Apr 2021cs.CLcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 3 Apr 2021.

Builds on

6 of 28 references.

References · 6

  1. 2020Mapping Languages: The Corpus of Global Language Usecited by 42
  2. 2017Dialectometric analysis of language variation in Twittercited by 14
  3. 2019Modeling Global Syntactic Variation in English Using Dialect Classificationcited by 13
  4. 2013Where in the World are You? Geolocation and Language Identification in Twittercited by 11
  5. 2020Mapping Languages and Demographics with Georeferenced Corporacited by 6
  6. 2013Using Robust PCA to estimate regional characteristics of language use from geo-tagged Twitter messagescited by 2

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 2.

10 papers.

  1. 2023Quantifying the Dialect Gap and its Correlates Across Languagescited by 33
  2. 2023Split-and-Denoise: Protect large language model inference with local differential privacycited by 30
  3. 2021Learned Construction Grammars Converge Across Registers Given Increased Exposurecited by 6
  4. 2022Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languagescited by 6
  5. 2024Pre-Trained Language Models Represent Some Geographic Populations Better Than Otherscited by 6
  6. 2022Corpus Similarity Measures Remain Robust Across Diverse Languagescited by 4
  7. 2022Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measurescited by 3
  8. 2022Register Variation Remains Stable Across 60 Languagescited by 3
  9. 2023Variation and Instability in Dialect-Based Embedding Spacescited by 3
  10. 2024Validating and Exploring Large Geographic Corporacited by 0