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Alan Clinton Bale [3]Alan C. Bale [1]
  1. Alan Clinton Bale (2011). Scales and Comparison Classes. Natural Language Semantics 19 (2):169-190.
    This paper discusses comparison classes—sets that relativize the interpretation of gradable adjectives, often specified with for-clauses as in John is smart for a linguist. Such a discussion ultimately lends support to the thesis that scales, degrees, measure functions, and linear orders are grammatically derived from more basic relations between individuals. Three accounts of comparison classes are compared and evaluated. The first proposes that such classes serve as an argument to a function that determines a standard of comparison. The second maintains (...)
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  2. Alan Clinton Bale (2008). A Universal Scale of Comparison. Linguistics and Philosophy 31 (1):1-55.
    Comparative constructions form two classes, those that permit direct comparisons (comparisons of measurements as in Seymour is taller than he is wide) and those that only allow indirect comparisons (comparisons of relative positions on separate scales as in Esme is more beautiful than Einstein is intelligent). In contrast with other semantic theories, this paper proposes that the interpretation of the comparative morpheme remains the same whether it appears in sentences that compare individuals directly or indirectly. To develop a unified account, (...)
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  3. Alan Clinton Bale, The Universal Scale and the Semantics of Comparison.
    Comparative constructions allow individuals to be compared according to different properties. Such comparisons form two classes, those that permit direct, comparisons (comparisons of measurements as in Seymour is taller than he is wide) and those that only allow indirect comparisons (comparisons of relative positions on separate scales as in Esme is more beautiful than Einstein is intelligent). Traditionally, these two types of comparisons have been associated with an ambiguity in the interpretations of the comparative and equative morphemes (see, Bartsch & (...)
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  4. Thomas R. Shultz & Alan C. Bale (2006). Neural Networks Discover a Near-Identity Relation to Distinguish Simple Syntactic Forms. Minds and Machines 16 (2):107-139.
    Computer simulations show that an unstructured neural-network model [Shultz, T. R., & Bale, A. C. (2001). Infancy, 2, 501–536] covers the essential features␣of infant learning of simple grammars in an artificial language [Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999). Science, 283, 77–80], and generalizes to examples both outside and inside of the range of training sentences. Knowledge-representation analyses confirm that these networks discover that duplicate words in the sentences are nearly identical and that they (...)
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