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  1. Contextual Vocabulary Acquisition: from Algorithm to Curriculum.Michael W. Kibby & William J. Rapaport - 2014 - In Michael W. Kibby & William J. Rapaport (eds.), Contextual Vocabulary Acquisition: from Algorithm to Curriculum. pp. 107-150.
    Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a (not the) meaning for an unknown word from its “context”, without external sources of help such as dictionaries or people. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new (...)
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  • Steps toward formalizing context.Varol Akman & Mehmet Surav - 1996 - AI Magazine 17 (3):55-72.
    The importance of contextual reasoning is emphasized by various researchers in AI. (A partial list includes John McCarthy and his group, R. V. Guha, Yoav Shoham, Giuseppe Attardi and Maria Simi, and Fausto Giunchiglia and his group.) Here, we survey the problem of formalizing context and explore what is needed for an acceptable account of this abstract notion.
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  • Meinongian Semantics and Artificial Intelligence.William J. Rapaport - 2013 - Humana Mente 6 (25):25-52.
    This essay describes computational semantic networks for a philosophical audience and surveys several approaches to semantic-network semantics. In particular, propositional semantic networks are discussed; it is argued that only a fully intensional, Meinongian semantics is appropriate for them; and several Meinongian systems are presented.
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  • Computers Are Syntax All the Way Down: Reply to Bozşahin.William J. Rapaport - 2019 - Minds and Machines 29 (2):227-237.
    A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.
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  • Interestingness: Controlling inferences.Roger C. Schank - 1979 - Artificial Intelligence 12 (3):273-297.
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  • Language and Memory.Roger C. Schank - 1980 - Cognitive Science 4 (3):243-284.
    This paper outlines some of the issues and basic philosophy that have guided my work and that of my students in the last ten years. It describes the progression of conceptual representational theories developed during that time, as well as some of the research models built to implement those theories. The paper concludes with a discussion of my most recent work in the area of modelling memory. It presents a theory of MOPs (Memory Organization Packets), which serve as both processors (...)
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  • A preferential, pattern-seeking, Semantics for natural language inference.Yorick Wilks - 1975 - Artificial Intelligence 6 (1):53-74.
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  • Response to Dresher and Hornstein.Roger C. Schank & Robert Wilensky - 1977 - Cognition 5 (2):133-145.
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