A Computational Construction Grammar Framework for Modelling Signed Languages

Liesbet De Vos, Paul Van Eecke, Katrien Beuls


Abstract
Constructional approaches to signed languages are becoming increasingly popular within sign language linguistics. Current approaches, however, focus primarily on theoretical description, while formalization and computational implementation remain largely unexplored. This paper provides an initial step towards addressing this gap by studying and operationalizing the core mechanisms required for representing and processing manual signed forms using computational construction grammar. These include a phonetic representation of individual manual signs and a formal representation of the complex temporal synchronization patterns between them. The implemented mechanisms are integrated into Fluid Construction Grammar and are available as a module within the Babel software library. Through an interactive web demonstration, we illustrate how this module lays the groundwork for future computational exploration of constructions that bidirectionally map between signed forms and their meanings.
Anthology ID:
2025.cxgsnlp-1.1
Volume:
Proceedings of the Second International Workshop on Construction Grammars and NLP
Month:
September
Year:
2025
Address:
Düsseldorf, Germany
Editors:
Claire Bonial, Melissa Torgbi, Leonie Weissweiler, Austin Blodgett, Katrien Beuls, Paul Van Eecke, Harish Tayyar Madabushi
Venues:
CxGsNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–12
Language:
URL:
https://preview.aclanthology.org/iwcs-25-ingestion/2025.cxgsnlp-1.1/
DOI:
Bibkey:
Cite (ACL):
Liesbet De Vos, Paul Van Eecke, and Katrien Beuls. 2025. A Computational Construction Grammar Framework for Modelling Signed Languages. In Proceedings of the Second International Workshop on Construction Grammars and NLP, pages 1–12, Düsseldorf, Germany. Association for Computational Linguistics.
Cite (Informal):
A Computational Construction Grammar Framework for Modelling Signed Languages (De Vos et al., CxGsNLP 2025)
Copy Citation:
PDF:
https://preview.aclanthology.org/iwcs-25-ingestion/2025.cxgsnlp-1.1.pdf