Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
A Version Space Approach to Learning Context-free Grammars
Machine Learning
A minimum description length approach to grammar inference
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Grammatical inference by Hill Climbing
Information Sciences: an International Journal
Incremental Learning of Context Free Grammars
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Hierarchical Skills from Observation
DS '02 Proceedings of the 5th International Conference on Discovery Science
Learning to Parse from a Treebank: Combining TBL and ILP
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Using Contextual Representations to Efficiently Learn Context-Free Languages
The Journal of Machine Learning Research
A survey of grammatical inference methods for natural language learning
Artificial Intelligence Review
A memetic grammar inference algorithm for language learning
Applied Soft Computing
On the need to bootstrap ontology learning with extraction grammar learning
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Inferring grammar rules of programming language dialects
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Acquisition of hierarchical reactive skills in a unified cognitive architecture
Cognitive Systems Research
Unsupervised grammar inference using the minimum description length principle
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A syntactic approach to robot imitation learning using probabilistic activity grammars
Robotics and Autonomous Systems
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We examine the role of simplicity in directing the induction of context-free grammars from sample sentences. We present a rational reconstruction of Wolff's SNPR - the GRIDS system - which incorporates a bias toward grammars that minimize description length. The algorithm alternates between merging existing nonterminal symbols and creating new symbols, using a beam search to move from complex to simpler grammars. Experiments suggest that this approach can induce accurate grammars and that it scales reasonably to more difficult domains.