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Modelling Context-Dependence: Ellipsis in Conversational Dialogue

(Keynote Talk)
  • Ruth Kempson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6967)

Abstract

Despite recognition that context dependence is endemic to natural language, arguably one of its core properties, standard grammar formalisms remain poor vehicles for expressing this. Grammars are standardly defined in terms of principles underpinning structures inhabited by sentence strings to yield a concept of sentence wellformedness, these being defined independently of any attribute of performance; and all data not characterisable in these terms are seen as in some sense peripheral, requiring grammar-external explication. Phenomena displaying context dependence all pose problems for this methodology, context dependence being no respecter of sentence boundaries. Ellipsis poses this challenge in a particularly acute way. A reasonable goal for accounts of ellipsis might be seen as one providing formal explication of the informal insight that ellipsis constitutes the license for expressions to be omitted if recoverable from context, with ellipsis data taken as a window on the requisite concept of context for language construal. But such a goal isn’t even addressed as a desideratum in current accounts. Ellipsis is seen as falling into two major types: sentence-internal and discourse ellipsis; and even within the former type, there is further bifurcation into those types of ellipsis characterisable as semantically grounded (Dalrymple et al 1991) and those requiring syntactic characterisation with a number of discrete structural types (Merchant 2009). And, with a pragmatic remainder falling outside grammar-based characterisation (Stainton 2006), the phenomenon is seen as irreducibly “fractally heterogeneous” (Ginzburg and Cooper 2004).

Keywords

Propositional Content Context Dependence Grammar Formalism Sentence Boundary Ellipsis Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ruth Kempson
    • 1
  1. 1.King’s College LondonUK

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