Abstract
It is proved that for any k, the class of classical categorial grammars that assign at most k types to each symbol in the alphabet is learnable, in the Gold (1967) sense of identification in the limit from positive data. The proof crucially relies on the fact that the concept known as finite elasticity in the inductive inference literature is preserved under the inverse image of a finite-valued relation. The learning algorithm presented here incorporates Buszkowski and Penn's (1990) algorithm for determining categorial grammars from input consisting of functor-argument structures.
Keywords categorial grammar  finite elasticity  functor-argument struture  identification in the limit  inductive inference  learnability
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DOI 10.1007/BF00173697
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References found in this work BETA

The Proper Treatment of Quantification in Ordinary English.Richard Montague - 1973 - In Patrick Suppes, Julius Moravcsik & Jaakko Hintikka (eds.), Approaches to Natural Language. Dordrecht. pp. 221--242.
The Proper Treatment of Quantification in Ordinary English.Richard Montague - 1974 - In Richmond H. Thomason (ed.), Formal Philosophy. Yale University Press.
The Mathematics of Sentence Structure.Joachim Lambek - 1968 - Journal of Symbolic Logic 33 (4):627-628.
Language and Information.Yehoshua Bar-Hillel - 1965 - Journal of Symbolic Logic 30 (3):382-385.

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Learning Categorial Grammar by Unification with Negative Constraints.Jacek Marciniec - 1994 - Journal of Applied Non-Classical Logics 4 (2):181-200.

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