15 found
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  1.  19
    Computational semantics: an introduction to artificial intelligence and natural language comprehension.Eugene Charniak & Yorick Wilks (eds.) - 1976 - New York: distributors for the U.S.A. and Canada, Elsevier/North Holland.
    Linguistics. Artificial intelligence. Related fields. Computation.
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  2.  16
    Passing Markers: A Theory of Contextual Influence in Language Comprehension.Eugene Charniak - 1983 - Cognitive Science 7 (3):171-190.
    Most Artificial Intelligence theories of language either assume a syntactic component which serves as “front end” for the rest of the system, or else reject all attempts at distinguishing modules within the comprehension system. In this paper we will present an alternative which, while keeping modularity, will account for several puzzles for typical “syntax first” theories. The major addition to this theory is a “marker passing” (or “spreading activation”) component, which operates in parallel to the normal syntactic component.
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  3.  7
    On the use of framed knowledge in language comprehension.Eugene Charniak - 1978 - Artificial Intelligence 11 (3):225-265.
  4.  5
    Cost-based abduction and MAP explanation.Eugene Charniak & Solomon Eyal Shimony - 1994 - Artificial Intelligence 66 (2):345-374.
  5.  7
    A Bayesian model of plan recognition.Eugene Charniak & Robert P. Goldman - 1993 - Artificial Intelligence 64 (1):53-79.
  6.  4
    A common representation for problem-solving and language-comprehension information.Eugene Charniak - 1981 - Artificial Intelligence 16 (3):225-255.
  7.  7
    Motivation analysis, abductive unification, and nonmonotonic equality.Eugene Charniak - 1988 - Artificial Intelligence 34 (3):275-295.
  8.  25
    Framed PAINTING: The Representation of a Common Sense Knowledge Fragment.Eugene Charniak - 1977 - Cognitive Science 1 (4):235-264.
    This paper presents a “frame” representation for common sense knowledge and uses it to formalize our knowledge of “mundane” painting (walls; not portraits). These frames. while designed to aid a computer program to understand stories about the painting process, should be of use to programs which attempt to actually carry out the activity. The paper stresses a “deep” understanding of the activity so that the representation indicates not only what steps to carry out, but also how to do them, and (...)
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  9.  8
    The Case‐Slot Identity Theory.Eugene Charniak - 1981 - Cognitive Science 5 (3):285-292.
    Many people have noted the similarities between case theories in linguistics and frame representations in artificial intelligence. In particular, the cases of a verb seem to correspond to the slots of a frame. This has led many people, including Fillmore [1977] and Winston [1977] to assert that cases and slots are one and the same. This hypothesis has not attracted much attention, probably because the notion of “slot” in frame representations is so underconstrained that the theory would seem to be (...)
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  10.  7
    Taggers for parsers.Eugene Charniak, Glenn Carroll, John Adcock, Anthony Cassandra, Yoshihiko Gotoh, Jeremy Katz, Michael Littman & John McCann - 1996 - Artificial Intelligence 85 (1-2):45-57.
  11. Context recognition in language comprehension.Eugene Charniak - 1982 - In W. Lehnert (ed.), Strategies for Natural Language Processing. Lawrence Erlbaum. pp. 435--454.
  12.  21
    How to register dissatisfaction with A.I.Eugene Charniak - 1978 - Behavioral and Brain Sciences 1 (2):230-231.
  13. Inference and knowledge.Eugene Charniak - 1976 - In Eugene Charniak & Yorick Wilks (eds.), Computational semantics: an introduction to artificial intelligence and natural language comprehension. New York: distributors for the U.S.A. and Canada, Elsevier/North Holland. pp. 1--21.
     
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  14.  6
    Referenz und Fragebeantwortung in einfachen Erzählungen.Eugene Charniak - 1977 - In Peter Eisenberg (ed.), Semantik Und Künstliche Intelligenz: Beiträge Zur Automatischen Sprachbearbeitung Ii. De Gruyter. pp. 21-38.
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  15.  34
    Some aspirin for Dasein.Eugene Charniak - 1980 - Behavioral and Brain Sciences 3 (1):74-74.
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