Modelling verb selection within argument structure constructions

Yevgen Matusevych, Afra Alishahi, Albert Backus

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Abstract

This article looks into the nature of cognitive associations between verbs and argument structure constructions (ASCs). Existing research has shown that distributional and semantic factors affect speakers' choice of verbs in ASCs. A formal account of this theory has been proposed by Ellis, O'Donnell, and Römer, who show that the frequency of production of verbs within an ASC can be predicted from joint verb–construction frequency, contingency of verb–construction mapping, and prototypicality of verb meaning. We simulate the verb production task using a computational model of ASC learning, and compare its performance to the available human data. To account for individual variation between speakers and for order of verb preference, we carry out two additional analyses. We then compare a number of prediction models with different variables, and propose a refined account of verb selection within ASCs: overall verb frequency is an additional factor affecting verb selection, while the effects of joint frequency and contingency may be combined rather than independent.
Original languageEnglish
Pages (from-to)1215-1244
Number of pages29
JournalLanguage, Cognition and Neuroscience
Volume31
Issue number10
Early online date30 Jun 2016
DOIs
Publication statusPublished - 2017

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contingency
semantics
Modeling
Argument Structure
Verbs
learning
performance
Contingency
Verb Production
Prototypicality
Computational Model
Individual Variation
Prediction
Verb Meaning

Cite this

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title = "Modelling verb selection within argument structure constructions",
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Modelling verb selection within argument structure constructions. / Matusevych, Yevgen; Alishahi, Afra; Backus, Albert.

In: Language, Cognition and Neuroscience, Vol. 31, No. 10, 2017, p. 1215-1244.

Research output: Contribution to journalArticleScientificpeer-review

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AB - This article looks into the nature of cognitive associations between verbs and argument structure constructions (ASCs). Existing research has shown that distributional and semantic factors affect speakers' choice of verbs in ASCs. A formal account of this theory has been proposed by Ellis, O'Donnell, and Römer, who show that the frequency of production of verbs within an ASC can be predicted from joint verb–construction frequency, contingency of verb–construction mapping, and prototypicality of verb meaning. We simulate the verb production task using a computational model of ASC learning, and compare its performance to the available human data. To account for individual variation between speakers and for order of verb preference, we carry out two additional analyses. We then compare a number of prediction models with different variables, and propose a refined account of verb selection within ASCs: overall verb frequency is an additional factor affecting verb selection, while the effects of joint frequency and contingency may be combined rather than independent.

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