Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Handbook of formal languages, vol. 3
The EMILE 4.1 Grammar Induction Toolbox
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Inducing grammars from sparse data sets: a survey of algorithms and results
The Journal of Machine Learning Research
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Limitations of current grammar induction algorithms
ACL '07 Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop
A bibliographical study of grammatical inference
Pattern Recognition
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Grammatical inference, also known as Grammar Induction, is about the problem of learning structural models from data. For decades researchers have been trying to devise formal and detailed grammars that would capture the observed regularities of language. This paper presents a comprehensive solution for efficient language acquisition by a novel semi-supervised algorithm that learns a streamlined representation of linguistic structures from a plain natural-language corpus. The input datasets are ATIS dataset and sentences from children's literature. The proposed algorithm generates rules from the given corpora and using the learned rules new sentences are generated. Performance of the algorithm is evaluated based on two measures – recall and precision. The recall was 0.935 and precision was 0.916. The results were found to be better than with other algorithms, such as EMILE, ADIOS and GCS. The running time of the algorithm is tested by varying the size of the dataset. It has shown a linear increment in time with the size of dataset.