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Article published In:
International Journal of Corpus Linguistics
Vol. 29:4 (2024) ► pp.507–533

Down-sampling from hierarchically structured corpus data

ORCID logo | University of Bamberg
Resource constraints often force researchers to downsize the list of tokens returned by a corpus query. This paper sketches a methodology for down-sampling and offers a survey of current practices. We build on earlier work and extend the evaluation of down-sampling designs to settings where tokens are clustered by text file and lexeme. Our case study deals with third-person present-tense verb inflection in Early Modern English and focuses on five predictors: year, gender, genre, frequency, and phonological context. We evaluate two strategies for selecting 2,000 (out of 11,645) tokens: simple down-sampling, where each hit has the same selection probability; and structured down-sampling, where this probability is inversely proportional to the author- and verb-specific token count. We form 500 subsamples using each scheme and compare regression results to a reference model fit to the full set of cases. We observe that structured down-sampling shows better performance on several evaluation criteria.
Keywords: down-sampling, thinning, methodology, data structure, study design
Article outline
  • 1.Introduction
  • 2.Down-sampling in corpus-based work
    • 2.1Down-sampling designs: Design features and terminology
    • 2.2A survey of down-sampling designs in corpus-based work
    • 2.3Previous methodological work
  • 3.Methodology
    • 3.1Case study and corpus data
      • 3.1.1Third-person verb inflection in Early Modern English
      • 3.1.2Data preparation
      • 3.1.3Data structure
    • 3.2Evaluation method
      • 3.2.1Implementation of down-sampling designs
      • 3.2.2The reference model
      • 3.2.3Evaluation of down-sampling designs
  • 4.Results
  • 5.Summary and outlook
  • Acknowledgements
  • Notes
  • References
Published online: 25 March 2024
DOI logo
https://doi.org/10.1075/ijcl.23079.son
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