Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity
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.
Keywords
Cite
@article{arxiv.2501.06536,
title = {Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity},
author = {Adam Nohejl and Taro Watanabe},
journal= {arXiv preprint arXiv:2501.06536},
year = {2025}
}
Comments
Pre-print, to be presented at the NLP Meeting 2025 (www.anlp.jp - NON-REVIEWED)