Modeling Color Terminology Across Thousands of Languages

TL;DR

This study uses computational linguistic measures on cross-linguistic data to evaluate and refine the Berlin and Kay color term hypotheses, supporting their universality and suggesting a spectrum rather than a strict dichotomy.

Contribution

Introduces 14 empirical metrics to analyze color terminology across languages, validating and extending Berlin and Kay's hypotheses with quantitative evidence.

Findings

Strong correlation (gamma=0.96) with Berlin and Kay's color term partition

Empirical support for the universal acquisition sequence of color terms

Suggests viewing color term categorization as a spectrum rather than a binary division

Abstract

There is an extensive history of scholarship into what constitutes a "basic" color term, as well as a broadly attested acquisition sequence of basic color terms across many languages, as articulated in the seminal work of Berlin and Kay (1969). This paper employs a set of diverse measures on massively cross-linguistic data to operationalize and critique the Berlin and Kay color term hypotheses. Collectively, the 14 empirically-grounded computational linguistic metrics we design---as well as their aggregation---correlate strongly with both the Berlin and Kay basic/secondary color term partition (gamma=0.96) and their hypothesized universal acquisition sequence. The measures and result provide further empirical evidence from computational linguistics in support of their claims, as well as additional nuance: they suggest treating the partition as a spectrum instead of a dichotomy.