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        The empirical base of linguistics: Grammaticality judgments and linguistic methodology

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        Author(s)
        T. Schütze, Carson
        Collection
        Knowledge Unlatched (KU)
        Language
        English
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        Abstract
        Throughout much of the history of linguistics, grammaticality judgments – intuitions about the well-formedness of sentences – have constituted most of the empirical base against which theoretical hypothesis have been tested. Although such judgments often rest on subtle intuitions, there is no systematic methodology for eliciting them, and their apparent instability and unreliability have led many to conclude that they should be abandoned as a source of data.
        URI
        http://library.oapen.org/handle/20.500.12657/32839
        Keywords
        linguistic methodology; grammaticality judgements; intuition; Noam Chomsky; Parsing; Syntax
        DOI
        10.26530/OAPEN_603356
        ISBN
        9783946234043
        OCN
        945783708
        Publisher
        Language Science Press
        Publisher website
        https://langsci-press.org/
        Publication date and place
        2016
        Grantor
        • Knowledge Unlatched
        Series
        Classics in Linguistics, 3
        Classification
        Linguistics
        Pages
        244
        Public remark
        Relevant Wikipedia pages: Grammar - https://en.wikipedia.org/wiki/Grammar; Grammaticality - https://en.wikipedia.org/wiki/Grammaticality; Intuition - https://en.wikipedia.org/wiki/Intuition; Linguistics - https://en.wikipedia.org/wiki/Linguistics; Noam Chomsky - https://en.wikipedia.org/wiki/Noam_Chomsky; Parsing - https://en.wikipedia.org/wiki/Parsing; Syntax - https://en.wikipedia.org/wiki/Syntax
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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