Dispersion Measures as Predictors of Lexical Decision Time, Word Familiarity, and Lexical Complexity
This work addresses the need for external validation of dispersion measures in linguistics and cognitive science, though it is incremental as it builds on existing measures.
The study tackled the problem of validating dispersion measures for predicting lexical decision time, word familiarity, and lexical complexity across five languages, finding that the logarithm of range outperformed log-frequency and more complex measures as a predictor.
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.