Fluid Construction Grammar: State of the Art and Future Outlook

Katrien Beuls, Paul Van Eecke

Research output: Chapter in Book/Report/Conference proceedingConference paper


Fluid Construction Grammar (FCG) is a computational framework that provides a formalism for representing construction grammars and a processing engine that supports construction-based language comprehension and production. FCG is conceived as a computational operationalisation of the basic tenets of construction grammar. It thereby aims to establish more solid foundations for constructionist theories of language, while expanding their application potential in the fields of artificial intelligence and natural language understanding. This paper aims to provide a brief introduction to the FCG research programme, reflecting on what has been achieved so far and identifying key challenges for the future.
Original languageEnglish
Title of host publicationProceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
PublisherAssociation for Computational Linguistics
Number of pages10
ISBN (Electronic)978-1-959429-35-7
Publication statusPublished - 2023
EventGeorgetown University Round Table: GURT/SyntaxFest 2023 - Washington, United States
Duration: 9 Mar 202312 Mar 2023

Publication series

NameCxGsNLP 2023 - 1st International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023), Proceedings of the Conference


WorkshopGeorgetown University Round Table
Country/TerritoryUnited States
Internet address

Bibliographical note

Funding Information:
We would like to express our gratitude to the multi-talented team of researchers that have contributed to the FCG research programme in the past, and to the current team who ensures that the future of computational construction grammar looks brighter than ever: Luc Steels, Remi van Trijp, Jens Nevens, Tom Willaert, Lara Verheyen, Jonas Doumen, Jérôme Botoko Ekila, Veronica Schmalz, Liesbet De Vos, Alexane Jouglar and Jeroen Van Soest. The research reported on in this paper was financed by the Research Foundation Flanders (FWO) through a postdoctoral grant awarded to Paul Van Eecke (75929) and the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 951846 (MUHAI - https://www.muhai.org).

Publisher Copyright:
© 2023 Association for Computational Linguistics.


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