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International and Interdisciplinary Conference on Modeling and Using Context

CONTEXT 2017: Modeling and Using Context pp 203–212Cite as

On Conceivability and Existence in Linguistic Interpretation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10257))

Abstract

This paper discusses the role of existence presuppositions and conceivability in linguistic interpretation. In particular, it is discussed evidence that the non-emptiness requirement on universally-quantified propositions might be satisfied through access to a background of knowledge concerning what might be the case in the actual world, as opposed to a background of knowledge concerning what is actually the case. The evidence discussed comes from empirical observations on the behavior of the scalar implicature mechanism. The crucial piece evidence amounts to the fact that the entailment pattern with existentially-quantified propositions, which is crucial to generate the desired inference, is established when quantifiers range over non-existing but conceivable entities, but it cannot be established when quantifiers range over non-exiting and inconceivable entities.

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Author information

Authors and Affiliations

  1. Leibniz-Zentrum Allgemeine Sprachwissenschaft (ZAS), Berlin, Germany

    Salvatore Pistoia-Reda

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  1. Salvatore Pistoia-Reda
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Correspondence to Salvatore Pistoia-Reda .

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Editors and Affiliations

  1. Pierre and Marie Curie University , Paris, France

    Patrick Brézillon

  2. University of Maine , Orono, Maine, USA

    Roy Turner

  3. University of Genoa , Genoa, Italy

    Carlo Penco

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Pistoia-Reda, S. (2017). On Conceivability and Existence in Linguistic Interpretation. In: Brézillon, P., Turner, R., Penco, C. (eds) Modeling and Using Context. CONTEXT 2017. Lecture Notes in Computer Science(), vol 10257. Springer, Cham. https://doi.org/10.1007/978-3-319-57837-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-57837-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57836-1

  • Online ISBN: 978-3-319-57837-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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