The Strong, Weak, and Very Weak Finite Context and Kernel Properties

DOI10.1007/978-3-319-53733-7_5zbMath1485.68123OpenAlexW2588167082MaRDI QIDQ5738988

Makoto Kanazawa, Ryo Yoshinaka

Publication date: 1 June 2017

Published in: Language and Automata Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-53733-7_5

zbMATH Keywords

context-free languagesgrammatical inferencedistributional learningalgorithmic learningfinite context propertyfinite kernel property

Mathematics Subject Classification ID

Computational learning theory (68Q32) Formal languages and automata (68Q45) Grammars and rewriting systems (68Q42)

Related Items (2)

Distributional learning of conjunctive grammars and contextual binary feature grammars ⋮ Learning context-free grammars from positive data and membership queries

Cites Work

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• Optimization of LR(k) parsers
• Learning Conjunctive Grammars and Contextual Binary Feature Grammars
• Learning Context Free Grammars with the Finite Context Property: A Correction of A. Clark’s Algorithm
• Distributional Learning of Context-Free and Multiple Context-Free Grammars
• Distributional Learning of Abstract Categorial Grammars
• The syntactic concept lattice: Another algebraic theory of the context-free languages?
• Learning Context Free Grammars with the Syntactic Concept Lattice
• Distributional Learning of Some Nonlinear Tree Grammars
• Towards Dual Approaches for Learning Context-Free Grammars Based on Syntactic Concept Lattices
• A helpful result for proving inherent ambiguity

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