Learned Construction Grammars Converge Across Registers Given Increased Exposure

Dunn, Jonathan; Madabushi, Harish Tayyar

doi:10.18653/v1/2021.conll-1.21

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…CxG is a usage-based approach to syntax which, in practical terms, means that more item-specific constructions are learned first and then generalized into more schematic constructions (Nevens et al, 2022;Doumen et al, 2023). The grammar learning algorithm used in this paper is taken from previous work (Dunn, 2017;Dunn, 2018b;Dunn, 2019a;Dunn and Nini, 2021;Dunn and Tayyar Madabushi, 2021;Dunn, 2022), with the grammar trained from the same register as the dialectal data (tweets). Rather than describe the computational details of this line of work, this section instead analyzes constructions within the grammar as examples of the kinds of features used to model syntactic variation.…”

Section: Computational Construction Grammarmentioning

confidence: 99%

Syntactic variation across the grammar: modelling a complex adaptive system

Dunn

2023

Front. Complex Syst.

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While language is a complex adaptive system, most work on syntactic variation observes a few individual constructions in isolation from the rest of the grammar. This means that the grammar, a network which connects thousands of structures at different levels of abstraction, is reduced to a few disconnected variables. This paper quantifies the impact of such reductions by systematically modelling dialectal variation across 49 local populations of English speakers in 16 countries. We perform dialect classification with both an entire grammar as well as with isolated nodes within the grammar in order to characterize the syntactic differences between these dialects. The results show, first, that many individual nodes within the grammar are subject to variation but, in isolation, none perform as well as the grammar as a whole. This indicates that an important part of syntactic variation consists of interactions between different parts of the grammar. Second, the results show that the similarity between dialects depends heavily on the sub-set of the grammar being observed: for example, New Zealand English could be more similar to Australian English in phrasal verbs but at the same time more similar to UK English in dative phrases.

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Section: Computational Construction Grammarmentioning

confidence: 99%

Syntactic variation across the grammar: modelling a complex adaptive system

Dunn

2023

Front. Complex Syst.

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“…An external metric evaluates and compares constructicons using their performance when applied to a specific prediction task. Recent work has focused on the use of computational CxG for modelling individual differences (Dunn and Nini, 2021), register variation (Dunn and Tayyar Madabushi, 2021), and population-based dialectal differences (Dunn, 2018a(Dunn, , 2019cDunn and Wong, 2022). Because CxG is a usage-based paradigm, the definition of a construction that is referenced above depends on both entrenchment and idiomaticity.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Constructicon: Linguistic Analysis of a Computational CxG

Dunn¹

2023

Preprint

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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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Computational Construction Grammar

Dunn

2024

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This Element introduces a usage-based computational approach to Construction Grammar that draws on techniques from natural language processing and unsupervised machine learning. This work explores how to represent constructions, how to learn constructions from a corpus, and how to arrange the constructions in a grammar as a network. From a theoretical perspective, this Element examines how construction grammars emerge from usage alone as complex systems, with slot-constraints learned at the same time that constructions are learned. From a practical perspective, this work is accompanied by a Python package which enables linguists to incorporate construction grammars into their own corpus-based work. The computational experiments in this Element are important for testing the learnability, variability, and confirmability of Construction Grammar as a theory of language. All code examples will leverage the cloud computing platform Code Ocean to guide readers through implementation of these algorithms.

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Learned Construction Grammars Converge Across Registers Given Increased Exposure

Cited by 6 publications

References 32 publications

Syntactic variation across the grammar: modelling a complex adaptive system

Syntactic variation across the grammar: modelling a complex adaptive system

Exploring the Constructicon: Linguistic Analysis of a Computational CxG

Computational Construction Grammar

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