@inproceedings{simon-2023-constructions,
title = "Constructions, Collocations, and Patterns: Alternative Ways of Construction Identification in a Usage-based, Corpus-driven Theoretical Framework",
author = "Simon, G{\'a}bor",
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-1.2",
pages = "12--20",
abstract = "There is a serious theoretical and methodological dilemma in usage-based construction grammar: how to identify constructions based on corpus pattern analysis. The present paper provides an overview of this dilemma, focusing on argument structure constructions (ASCs) in general. It seeks to answer the question of how a data-driven construction grammatical description can be built on the collocation data extracted from corpora. The study is of meta-scientific interest: it compares theoretical proposals in construction grammar regarding how they handle co-occurrences emerging from a corpus. Discussing alternative bottom-up approaches to the notion of construction, the paper concludes that there is no one-to-one correspondence between corpus patterns and constructions. Therefore, a careful analysis of the former can empirically ground both the identification and the description of constructions.",
}
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<abstract>There is a serious theoretical and methodological dilemma in usage-based construction grammar: how to identify constructions based on corpus pattern analysis. The present paper provides an overview of this dilemma, focusing on argument structure constructions (ASCs) in general. It seeks to answer the question of how a data-driven construction grammatical description can be built on the collocation data extracted from corpora. The study is of meta-scientific interest: it compares theoretical proposals in construction grammar regarding how they handle co-occurrences emerging from a corpus. Discussing alternative bottom-up approaches to the notion of construction, the paper concludes that there is no one-to-one correspondence between corpus patterns and constructions. Therefore, a careful analysis of the former can empirically ground both the identification and the description of constructions.</abstract>
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%0 Conference Proceedings
%T Constructions, Collocations, and Patterns: Alternative Ways of Construction Identification in a Usage-based, Corpus-driven Theoretical Framework
%A Simon, Gábor
%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 simon-2023-constructions
%X There is a serious theoretical and methodological dilemma in usage-based construction grammar: how to identify constructions based on corpus pattern analysis. The present paper provides an overview of this dilemma, focusing on argument structure constructions (ASCs) in general. It seeks to answer the question of how a data-driven construction grammatical description can be built on the collocation data extracted from corpora. The study is of meta-scientific interest: it compares theoretical proposals in construction grammar regarding how they handle co-occurrences emerging from a corpus. Discussing alternative bottom-up approaches to the notion of construction, the paper concludes that there is no one-to-one correspondence between corpus patterns and constructions. Therefore, a careful analysis of the former can empirically ground both the identification and the description of constructions.
%U https://aclanthology.org/2023.cxgsnlp-1.2
%P 12-20
Markdown (Informal)
[Constructions, Collocations, and Patterns: Alternative Ways of Construction Identification in a Usage-based, Corpus-driven Theoretical Framework](https://aclanthology.org/2023.cxgsnlp-1.2) (Simon, CxGsNLP-SyntaxFest 2023)
ACL