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Evolving stochastic context-free grammars from examples using a minimum description length principle

posted on 2023-06-08, 00:24 authored by Bill Keller, Rüdi Lutz
This paper describes an evolutionary approach to the problem of inferring stochastic context-free grammars from finite language samples. The approach employs a genetic algorithm, with a fitness function derived from a minimum description length principle. Solutions to the inference problem are evolved by optimizing the parameters of a covering grammar for a given language sample. We provide details of our fitness function for grammars and present the results of a number of experiments in learning grammars for a range of formal languages. Keywords: grammatical inference, genetic algorithms, language modelling, formal languages, induction, minimum description length. Introduction Grammatical inference (Gold 1978) is a fundamental problem in many areas of artificial intelligence and cognitive science, including speech and language processing, syntactic pattern recognition and automated programming. Although a wide variety of techniques for automated grammatical inference have been devis..


Publication status

  • Published



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Presentation Type

  • paper

Event name

Proceedings of the Workshop on Automata Induction Grammatical Inference and Language Acquisition ICML-97

Event location

Nashville Tennessee July 12th 1997

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  • Informatics Publications

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  • Yes

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