Ease of learning explains semantic universals

Abstract

Semantic universals are properties of meaning shared by the languages of the world. We offer an explanation of the presence of such universals by measuring simplicity in terms of ease of learning, showing that expressions satisfying universals are simpler than those that do not according to this criterion. We measure ease of learning using tools from machine learning and analyze universals in a domain of function words (quantifiers) and content words (color terms). Our results provide strong evidence that semantic universals across both function and content words reflect simplicity as measured by ease of learning.