Foundations of logic programming
Foundations of logic programming
Identification of unions of languages drawn from an identifiable class
COLT '89 Proceedings of the second annual workshop on Computational learning theory
Computational learning of languages
Computational learning of languages
Bilinear logic in algebra and linguistics
Proceedings of the workshop on Advances in linear logic
Learnable classes of categorial grammars
Learnable classes of categorial grammars
Algebraic structures in categorial grammar
AMiLP '95 Proceedings of the first international AMAST workshop on Algebraic methods in language processing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Handbook of Logic and Language
Handbook of Logic and Language
Incomplete Information: Structure, Inference, Complexity
Incomplete Information: Structure, Inference, Complexity
Optimal unification and learning algorithms for categorial grammars
Fundamenta Informaticae
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Structural Equations in Language Learning
LACL '01 Proceedings of the 4th International Conference on Logical Aspects of Computational Linguistics
Semantic bootstrapping of type-logical grammar
Journal of Logic, Language and Information
k-Valued non-associative Lambek grammars are learnable from generalized functor-argument structures
Theoretical Computer Science - Logic, language, information and computation
Knowledge Representation Techniques (Studies in Fuzziness and Soft Computing)
Knowledge Representation Techniques (Studies in Fuzziness and Soft Computing)
Optimal Unification of Infinite Sets of Types
Fundamenta Informaticae
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We apply basic notions of the theory of rough sets, due to Pawlak [30, 31], to explicate certain properties of unification-based learning algorithms for categorial grammars, introduced in [6, 11] and further developed in e.g. [18, 19, 24, 25, 26, 9, 28, 14, 15, 3]. The outcomes of these algorithms can be used to compute both the lower and the upper approximation of the searched language with respect to the given, finite sample. We show that these methods are also appropriate for other kinds of formal grammars, e.g. context-free grammars and context-sensitive grammars.