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Liefke, Kristina and Sanders, Sam (2016): A Computable Solution to Partee's Temperature Puzzle. In: Li, Yanyan (ed.) : State-of-the-Art and Future Directions of Smart Learning. Singapore: Springer. pp. 175-190

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This paper presents a computable solution to Partee's temperature puzzle which uses one of the standard tools of mathematics and the exact sciences: countable approximation. Our solution improves upon the standard Montagovian solution to the puzzle (i) by providing computable natural language interpretations for this solution, (ii) by lowering the complexity of the types in the puzzle's interpretation, and (iii) by acknowledging the role of linguistic and communicative context in this interpretation. These improvements are made possible by interpreting natural language in a model that is inspired by the Kleene-Kreisel model of countable-continuous functionals. In this model, continuous functionals are represented by lower-type objects, called the associates of these functionals, which only contain countable information.

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