@inproceedings{dunn-2023-exploring,
title = "Exploring the Constructicon: Linguistic Analysis of a Computational {C}x{G}",
author = "Dunn, Jonathan",
editor = "Bonial, Claire and
Tayyar Madabushi, Harish",
booktitle = "Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)",
month = mar,
year = "2023",
address = "Washington, D.C.",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.cxgsnlp-1.1",
pages = "1--11",
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.",
}
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%0 Conference Proceedings
%T Exploring the Constructicon: Linguistic Analysis of a Computational CxG
%A Dunn, Jonathan
%Y Bonial, Claire
%Y Tayyar Madabushi, Harish
%S Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
%D 2023
%8 March
%I Association for Computational Linguistics
%C Washington, D.C.
%F dunn-2023-exploring
%X 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.
%U https://aclanthology.org/2023.cxgsnlp-1.1
%P 1-11
Markdown (Informal)
[Exploring the Constructicon: Linguistic Analysis of a Computational CxG](https://aclanthology.org/2023.cxgsnlp-1.1) (Dunn, CxGsNLP-SyntaxFest 2023)
ACL