Characteristic Sets for Polynomial Grammatical Inference
Machine Learning
Machine Learning
Machine Learning
Inferring Deterministic Linear Languages
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Polynomial-time identification of very simple grammars from positive data
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
A study of grammatical inference
A study of grammatical inference
Generating all permutations by context-free grammars in Chomsky normal form
Theoretical Computer Science - Algebraic methods in language processing
Corpus-based induction of syntactic structure: models of dependency and constituency
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Polynomial Identification in the Limit of Substitutable Context-free Languages
The Journal of Machine Learning Research
PAC-learning unambiguous NTS languages
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Efficient, correct, unsupervised learning of context-sensitive languages
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Distributional learning of some context-free languages with a minimally adequate teacher
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Learning context free grammars with the syntactic concept lattice
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Towards general algorithms for grammatical inference
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Covariance in Unsupervised Learning of Probabilistic Grammars
The Journal of Machine Learning Research
Theoretical Computer Science
A learnable representation for syntax using residuated lattices
FG'09 Proceedings of the 14th international conference on Formal grammar
Three learnable models for the description of language
LATA'10 Proceedings of the 4th international conference on Language and Automata Theory and Applications
Polynomial time learning of some multiple context-free languages with a minimally adequate teacher
FG'10/FG'11 Proceedings of the 15th and 16th international conference on Formal Grammar
Semantic separator learning and its applications in unsupervised Chinese text parsing
Frontiers of Computer Science: Selected Publications from Chinese Universities
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We present a polynomial algorithm for the inductive inference of a large class of context free languages, that includes all regular languages. The algorithm uses a representation which we call Binary Feature Grammars based on a set of features, capable of representing richly structured context free languages as well as some context sensitive languages. More precisely, we focus on a particular case of this representation where the features correspond to contexts appearing in the language. Using the paradigm of positive data and a membership oracle, we can establish that all context free languages that satisfy two constraints on the context distributions can be identified in the limit by this approach. The polynomial time algorithm we propose is based on a generalisation of distributional learning and uses the lattice of context occurrences. The formalism and the algorithm seem well suited to natural language and in particular to the modelling of first language acquisition.