Definite-clause set grammars: a formalism for problem solving
Journal of Logic Programming
The minimum consistent DFA problem cannot be approximated within any polynomial
Journal of the ACM (JACM)
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Prolog (3rd ed.): programming for artificial intelligence
Prolog (3rd ed.): programming for artificial intelligence
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Learning Context-Free Grammars with a Simplicity Bias
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Synthesizing Context Free Grammars from Sample Strings Based on Inductive CYK Algorithm
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Learning Context-Free Grammars from Partially Structured Examples
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Incremental Learning of Context Free Grammars
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Inferring Deterministic Linear Languages
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Towards Machine Learning of Grammars and Compilers of Programming Languages
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Incremental learning of cellular automata for parallel recognition of formal languages
DS'10 Proceedings of the 13th international conference on Discovery science
Gramin: a system for incremental learning of programming language grammars
Proceedings of the 4th India Software Engineering Conference
Hi-index | 0.00 |
This paper describes novel methods of learning general context free grammars from sample strings, which are implemented in Synapse system. Main features of the system are incremental learning, rule generation based on bottom-up parsing of positive samples, and search for rule sets. From the results of parsing, a rule generation process, called “bridging,” synthesizes the production rules that make up any lacking parts of an incomplete derivation tree for each positive string. To solve the fundamental problem of complexity for learning CFG, we employ methods of searching for non-minimum, semi-optimum sets of rules as well as incremental learning based on related grammars. One of the methods is search strategy called “serial search,” which finds additional rules for each positive sample and not to find the minimum rule set for all positive samples as in global search. The other methods are not to minimize nonterminal symbols in rule generation and to restrict the form of generated rules. The paper shows experimental results and compares various synthesis methods.