Keller, Bill and Lutz, Rüdi (1997) Evolving stochastic context-free grammars from examples using a minimum description length principle. In: Proceedings of the Workshop on Automata Induction Grammatical Inference and Language Acquisition ICML-97, Nashville Tennessee July 12th 1997.

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Abstract

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..

Item Type:	Conference or Workshop Item (Paper)
Schools and Departments:	School of Engineering and Informatics > Informatics
Depositing User:	Bill Keller
Date Deposited:	06 Feb 2012 19:53
Last Modified:	13 Apr 2012 10:47
URI:	http://sro.sussex.ac.uk/id/eprint/22760

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