Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity

Adam Nohejl, Taro Watanabe
1/11/2025
cs.CL

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Abstract

Various measures of dispersion have been proposed to paint a fuller picture of a word's distribution in a corpus, but only little has been done to validate them externally. We evaluate a wide range of dispersion measures as predictors of lexical decision time, word familiarity, and lexical complexity in five diverse languages. We find that the logarithm of range is not only a better predictor than log-frequency across all tasks and languages, but that it is also the most powerful additional variable to log-frequency, consistently outperforming the more complex dispersion measures. We discuss the effects of corpus part granularity and logarithmic transformation, shedding light on contradictory results of previous studies.

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Cite this paper

@article{nohejl2025dispersion,
  title  = {Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity},
  author = {Adam Nohejl and Taro Watanabe},
  year   = {2025},
  eprint = {2501.06536},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.06536v1}
}

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