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

arXiv: https://arxiv.org/abs/2501.06536 · HTML: https://mlsanity.com/paper/2501.06536/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 39 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary?](https://mlsanity.com/paper/2410.03240/) (2024)

## Abstract (arXiv)

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.