14 found
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  1. Ellipsis and higher-order unification.Mary Dalrymple, Stuart M. Shieber & Fernando C. N. Pereira - 1991 - Linguistics and Philosophy 14 (4):399 - 452.
    We present a new method for characterizing the interpretive possibilities generated by elliptical constructions in natural language. Unlike previous analyses, which postulate ambiguity of interpretation or derivation in the full clause source of the ellipsis, our analysis requires no such hidden ambiguity. Further, the analysis follows relatively directly from an abstract statement of the ellipsis interpretation problem. It predicts correctly a wide range of interactions between ellipsis and other semantic phenomena such as quantifier scope and bound anaphora. Finally, although the (...)
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  2.  52
    Evidence against the context-freeness of natural language.Stuart M. Shieber - 1985 - Linguistics and Philosophy 8 (3):333 - 343.
  3. Can automatic calculating machines be said to think?M. H. A. Newman, Alan M. Turing, Geoffrey Jefferson, R. B. Braithwaite & S. Shieber - 2004 - In Stuart M. Shieber (ed.), The Turing Test: Verbal Behavior as the Hallmark of Intelligence. MIT Press.
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  4. The Turing Test: Verbal Behavior as the Hallmark of Intelligence.Stuart M. Shieber (ed.) - 2004 - MIT Press.
    Stuart M. Shieber’s name is well known to computational linguists for his research and to computer scientists more generally for his debate on the Loebner Turing Test competition, which appeared a decade earlier in Communications of the ACM. 1 With this collection, I expect it to become equally well known to philosophers.
  5.  24
    An Introduction to Unification-Based Approaches to Grammar.Stuart M. Shieber - 1987 - Journal of Symbolic Logic 52 (4):1052-1054.
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  6. Lessons from a restricted Turing test.Stuart M. Shieber - 1994 - Communications of the Association for Computing Machinery 37:70-82.
  7.  90
    The Turing test as interactive proof.Stuart M. Shieber - 2007 - Noûs 41 (4):686–713.
    In 1950, Alan Turing proposed his eponymous test based on indistinguishability of verbal behavior as a replacement for the question "Can machines think?" Since then, two mutually contradictory but well-founded attitudes towards the Turing Test have arisen in the philosophical literature. On the one hand is the attitude that has become philosophical conventional wisdom, viz., that the Turing Test is hopelessly flawed as a sufficient condition for intelligence, while on the other hand is the overwhelming sense that were a machine (...)
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  8. Machine learning theory and practice as a source of insight into universal grammar.Stuartm Shieber - unknown
    In this paper, we explore the possibility that machine learning approaches to naturallanguage processing being developed in engineering-oriented computational linguistics may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory (...)
     
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  9.  61
    There Can Be No Turing-Test-Passing Memorizing Machines.Stuart M. Shieber - 2014 - Philosophers' Imprint 14.
    Anti-behaviorist arguments against the validity of the Turing Test as a sufficient condition for attributing intelligence are based on a memorizing machine, which has recorded within it responses to every possible Turing Test interaction of up to a fixed length. The mere possibility of such a machine is claimed to be enough to invalidate the Turing Test. I consider the nomological possibility of memorizing machines, and how long a Turing Test they can pass. I replicate my previous analysis of this (...)
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  10.  30
    Direct parsing of ID/LP grammars.Stuart M. Shieber - 1984 - Linguistics and Philosophy 7 (2):135 - 154.
  11.  7
    Agent decision-making in open mixed networks.Ya'akov Gal, Barbara Grosz, Sarit Kraus, Avi Pfeffer & Stuart Shieber - 2010 - Artificial Intelligence 174 (18):1460-1480.
  12. Interactions of scope and ellipsis.Stuart M. Shieber, Fernando C. N. Pereira & Mary Dalrymple - 1996 - Linguistics and Philosophy 19 (5):527 - 552.
    Systematic semantic ambiguities result from the interaction of the two operations that are involved in resolving ellipsis in the presence of scoping elements such as quantifiers and intensional operators: scope determination for the scoping elements and resolution of the elided relation. A variety of problematic examples previously noted - by Sag, Hirschbüihler, Gawron and Peters, Harper, and others - all have to do with such interactions. In previous work, we showed how ellipsis resolution can be stated and solved in equational (...)
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  13. Machine learning theory and practice as a source of insight into universal grammar.Shalom Lappin with S. Shieber - manuscript
  14.  12
    Plan recognition in exploratory domains.Yaʼakov Gal, Swapna Reddy, Stuart M. Shieber, Andee Rubin & Barbara J. Grosz - 2012 - Artificial Intelligence 176 (1):2270-2290.
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