A Grounded Typology of Word Classes

Coleman Haley, Sharon Goldwater, Edoardo Ponti


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.
Anthology ID:
2025.naacl-long.521
Volume:
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
April
Year:
2025
Address:
Albuquerque, New Mexico
Editors:
Luis Chiruzzo, Alan Ritter, Lu Wang
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10380–10399
Language:
URL:
https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.521/
DOI:
Bibkey:
Cite (ACL):
Coleman Haley, Sharon Goldwater, and Edoardo Ponti. 2025. A Grounded Typology of Word Classes. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 10380–10399, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):
A Grounded Typology of Word Classes (Haley et al., NAACL 2025)
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PDF:
https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.521.pdf