Modeling Color Terminology Across Thousands of Languages

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 2241–2250, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 2241 Modeling Color Terminology Across Thousands of Languages Arya D. McCarthy, Winston Wu, Aaron Mueller, Bill Watson, and David Yarowsky Department of Computer Science Johns Hopkins University, Baltimore, MD USA {arya,wswu,amueller,billwatson,yarowsky}@jhu.edu Abstract There is an extensive history of scholarship into what constitutes a “basic” color term, as well as a broadly attested acquisition se- quence of basic color terms across many lan- guages, 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. Col- lectively, the 14 empirically-grounded compu- tational linguistic metrics we design—as well as their aggregation—correlate strongly with both the Berlin and Kay basic/secondary color term partition (g = 0.96) and their hypothe- sized universal acquisition sequence. The mea- sures and result provide further empirical ev- idence from computational linguistics in sup- port of their claims, as well as additional nu- ance: they suggest treating the partition as a spectrum instead of a dichotomy. 1 Introduction How many colors are in the rainbow? An infinite number, but each language divides up perceptual space into a finite number of categories by giving names to colors. The seminal work on color cate- gories, by Berlin and Kay (1969, hereafter B&K), characterizes a universal evolutionary sequence for languages’ core colors (their basic color terms) and their corresponding categories, at each stage refining the partition of color space. A handful of criteria define basic color terms, in- cluding abstractness, monomorphemicity, and not being subsumed by a broader basic term. (See §2 for the complete list.) These criteria are accused of biasing analyses of color systems—especially in non-Western societies (Wierzbicka, 2006). To mitigate this bias, a pan-lingual approach to analyz- ing color systems may reveal general (“universal”) trends more reliably than smaller datasets. While Language Color Word Literal Gloss Welsh brown brown Italian marrone chestnut Persianیا هﻮﻬﻗ coffee + of Cantonese ar coffee + color Table 1: Examples of terms representing brown, aris- ing from four processes: borrowing (Welsh; from En- glish), null affixing (Italian), derivational affixing (Per- sian), and compounding (Cantonese). data are hard to find in the long tail of languages, we still aim to consider more than ever before— 2491 languages and dialects.1 We leverage natural language processing tools to operationalize long- standing literature on language universals. We provide a three-pronged investigation of the classic criteria for basic color terms, examining the degree to which color words are abstract (§5), monomorphemic/monolexemic (§6), and salient (§7). Our operationalization of these (B&K) cri- teria shows that individual features do not reflect the basic/non-basic divide. Nor is this divide bi- nary, as B&K suggest: We show that abstract- ness, monomorphemicity, and even salience do not cleanly divide colors. Nonetheless, by treating basicness as …

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