Exploring the Constructicon: Linguistic Analysis of a Computational CxG

Jonathan Dunn


Abstract
Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. Previous work has evaluated these unsupervised grammars using both internal metrics (for example, Minimum Description Length) and external metrics (for example, performance on a dialectology task). This paper instead takes a linguistic approach to evaluation, first learning a constructicon and then analyzing its contents from a linguistic perspective. This analysis shows that a learned constructicon can be divided into nine major types of constructions, of which Verbal and Nominal are the most common. The paper also shows that both the token and type frequency of constructions can be used to model variation across registers and dialects.
Anthology ID:
2023.cxgsnlp-1.1
Volume:
Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
Month:
March
Year:
2023
Address:
Washington, D.C.
Editors:
Claire Bonial, Harish Tayyar Madabushi
Venues:
CxGsNLP | SyntaxFest
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/2023.cxgsnlp-1.1
DOI:
Bibkey:
Cite (ACL):
Jonathan Dunn. 2023. Exploring the Constructicon: Linguistic Analysis of a Computational CxG. In Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023), pages 1–11, Washington, D.C.. Association for Computational Linguistics.
Cite (Informal):
Exploring the Constructicon: Linguistic Analysis of a Computational CxG (Dunn, CxGsNLP-SyntaxFest 2023)
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PDF:
https://aclanthology.org/2023.cxgsnlp-1.1.pdf
Video:
 https://aclanthology.org/2023.cxgsnlp-1.1.mp4