Grammatical Inference: Introduction and Survey-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
Learning context-free grammars from structural data in polynomial time
Theoretical Computer Science
An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Information Processing and Management: an International Journal
Bayesian learning of probabilistic language models
Bayesian learning of probabilistic language models
An introduction to genetic algorithms
An introduction to genetic algorithms
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Recent advances of grammatical inference
Theoretical Computer Science - Special issue on algorithmic learning theory
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Inference of Stochastic Regular Grammars by Massively Parallel Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Crossover, Macromutationand, and Population-Based Search
Proceedings of the 6th International Conference on Genetic Algorithms
Learning Stochastic Regular Grammars by Means of a State Merging Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Regular Grammatical Inference from Positive and Negative Samples by Genetic Search: the GIG Method
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
A Comparison of Optimization Techniques for Integrated Manufacturing Planning and Scheduling
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Learning language using genetic algorithms
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
A minimum description length approach to grammar inference
Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing
Improved Learning for Hidden Markov Models Using Penalized Training
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Bayesian grammar induction for language modeling
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Unsupervised induction of stochastic context-free grammars using distributional clustering
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Distributional phrase structure induction
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Grammatical inference by Hill Climbing
Information Sciences: an International Journal
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Grammar-Based Classifier System for Recognition of Promoter Regions
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
How evolutionary algorithms are applied to statistical natural language processing
Artificial Intelligence Review
Language structure using fuzzy similarity
IEEE Transactions on Fuzzy Systems
Learning to recognize video-based spatiotemporal events
IEEE Transactions on Intelligent Transportation Systems
Towards 3D modeling of interacting TM helix pairs based on classification of helix pair sequence
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
A memetic grammar inference algorithm for language learning
Applied Soft Computing
Automatic extractive text summarization based on fuzzy logic: a sentence oriented approach
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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This paper describes an evolutionary approach to the problem of inferring stochastic context-free grammars from finite language samples. The approach employs a distributed, steady-state genetic algorithm, with a fitness function incorporating a prior over the space of possible grammars. Our choice of prior is designed to bias learning towards structurally simpler grammars. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. Full details are given of our genetic algorithm (GA) and of our fitness function for grammars. We present the results of a number of experiments in learning grammars for a range of formal languages. Finally we compare the grammars induced using the GA-based approach with those found using the inside-outside algorithm. We find that our approach learns grammars that are both compact and fit the corpus data well.