Regular models of phonological rule systems
Computational Linguistics - Special issue on computational phonology
Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Inference of Reversible Languages
Journal of the ACM (JACM)
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Some Classes of Regular Languages Identifiable in the Limit from Positive Data
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Polynomial-time identification of very simple grammars from positive data
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Identification of function distinguishable languages
Theoretical Computer Science
Counter-Free Automata (M.I.T. research monograph no. 65)
Counter-Free Automata (M.I.T. research monograph no. 65)
Polynomial Identification in the Limit of Substitutable Context-free Languages
The Journal of Machine Learning Research
Parallelism Increases Iterative Learning Power
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Learning quantity insensitive stress systems via local inference
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Inferring grammars for mildly context sensitive languages in polynomial-time
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Formal and empirical grammatical inference
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011
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The left-to-right and right-to-left iterative languages are previously unnoticed subclasses of the regular languages of infinite size that are identifiable in the limit from positive data. Essentially, these language classes are the ones obtained by merging final states in a prefix tree and initial states in a suffix tree of the observed sample, respectively. Strikingly, these classes are also transparently related to the zero-reversible languages because some algorithms that learn them differ minimally from the ZR algorithm given in Angluin (1982). Second, they are part of the answer to the challenge provided by Muggleton (1990), who proposed mapping the space of language classes obtainable by a general state-merging algorithm IM1. Third, these classes are relevant to a hypothesis of how children can acquire sound patterns of their language--in particular, the hypothesis that all phonotactic patterns found in natural language are neighborhood-distinct (Heinz 2007).