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Cognitive Linguistics

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Volume 30, Issue 1

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The more data, the better: A usage-based account of the English comparative correlative construction

Thomas Hoffmann
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  • English and American Studies, Catholic University of Eichstätt-Ingolstadt, Eichstätt, Germany
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/ Jakob Horsch / Thomas Brunner
Published Online: 2018-11-28 | DOI: https://doi.org/10.1515/cog-2018-0036

Abstract

Languages are complex systems that allow speakers to produce novel grammatical utterances. Yet, linguists differ as to how general and abstract they think the mental representation of speakers have to be to give rise to this grammatical creativity. In order to shed light on these questions, the present study looks at one specific construction type, English comparative correlatives, that turns out to be particularly interesting in this context: on the one hand it has been described in terms of one of the most abstract and general syntactic rules, on the other hand it shows specific idiomatic structures that are often produced without any variation (e.g. the more, the merrier). While the syntax and semantics of the English Comparative Correlative (CC) construction have received considerable attention in the literature, so far only a small number of usage-based analyses have been published on the topic. These either only relied on small databases or focussed only on the productivity of one slot in the construction. In contrast to this, the present study analyses more than 1,400 CC tokens sampled from COCA. The results of the present study yield important results concerning English CC constructions, including the schematicity and generality of their mental representations.

Keywords: construction grammar; syntax; comparative correlatives; usage-based approach

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About the article

Received: 2018-04-03

Accepted: 2018-07-06

Revised: 2018-07-04

Published Online: 2018-11-28

Published in Print: 2019-02-25


This study was supported in part by a German Research Foundation (DFG) grant (HO 3904/5-1).


Citation Information: Cognitive Linguistics, Volume 30, Issue 1, Pages 1–36, ISSN (Online) 1613-3641, ISSN (Print) 0936-5907, DOI: https://doi.org/10.1515/cog-2018-0036.

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