Learning Context-Free Grammars with a Simplicity Bias
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Using Grammatical Inference to Automate Information Extraction from the Web
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Programming Spoken Dialogs Using Grammatical Inference
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Estimating Grammar Parameters Using Bounded Memory
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Evolutionary induction of stochastic context free grammars
Pattern Recognition
A demonstration-based approach for designing domain-specific modeling languages
Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion
Creating domain-specific modeling languages using by-demonstration technique
Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion
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A cost function is developed, based on information-theoretic concepts, that measures the complexity of a stochastic context-free grammar, as well as the discrepancy between its language and a given stochastic language sample. This function is used to guide a search procedure that finds simple grammars whose languages are good fits to a sample. Reasonable results have been obtained in a variety of cases, including parenthesis and addition strings, Basic English (the first 25 sentences in English Through Pictures) and chain-encoded chromosome boundaries.