A Grounded Typology of Word Classes

Abstract

In this work, we propose a grounded approach to meaning in language typology. Using images captioned across languages, we can treat the images as an empirical language agnostic representation of meaning, allowing the quantification of language function and semantics. Using principles from information theory, we define “groundedness”, an empirical measure of contextual semantic contentfulness which can be computed using multilingual (vision-and-)language models. As an initial application, we apply this measure to the typology of word classes. We find our measure captures the contentfulness asymmetry between functional (grammatical) and lexical (content) classes across languages, but contradicts the view that functional classes do not convey content. We release a dataset of groundedness scores for 30 languages. Our results suggest that the grounded typology approach can provide quantitative evidence about semantic function in language.