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Learning Context-Free Grammars using an Evolutionary Approach

by: Marjan Mernik, Matej Črepinšek, Goran Gerlič, Viljem Žumer, Barrett R. Bryant, and Alan Sprague

(2003) .

Resources (URL, PDF, PS...)

URL:	http://www.comp.mq.edu.au/~asloane/plrg/reading/genPar03.pdf

Machine learning of grammars finds many applications in syntactic pattern recognition, computational biology, natural language acquisition, etc. In this paper a new application of grammatical inference is suggested. Development of domain-specific languages is a hard problem for domain experts not versed in programming language design. We believe that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are the using genetic programming approach in grammatical inference. Grammar-specific heuristic operators and non-random construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by small examples where underlying context-free grammars are successfully inferred.

BibTeX record

@techreport{Mernik:2003,
  abstract = {Machine learning of grammars finds many applications in syntactic pattern recognition, computational biology, natural language acquisition, etc. In this paper a new application of grammatical inference is suggested. Development of domain-specific languages is a hard problem for domain experts not versed in programming language design. We believe that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are the using genetic programming approach in grammatical inference. Grammar-specific heuristic operators and non-random construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by small examples where underlying context-free grammars are successfully inferred.},
  added-at = {2007-12-14T02:43:18.000+0100},
  author = {Mernik, Marjan and {\v C}repin{\v s}ek, Matej and Gerli{\v c}, Goran and {\v Z}umer, Viljem and Bryant, Barrett R. and Sprague, Alan},
  biburl = {http://www.bibsonomy.org/bibtex/22e2abadb70a8a0c33ee0f138128ae214/diego_ma},
  institution = {University of Maribor and The University of Alabama at Birginham},
  interhash = {f71371a5d08a2bf4ce5fa746880ad017},
  intrahash = {2e2abadb70a8a0c33ee0f138128ae214},
  keywords = {machine_learning grammar},
  timestamp = {2007-12-14T02:43:18.000+0100},
  title = {Learning Context-Free Grammars using an Evolutionary Approach},
  url = {http://www.comp.mq.edu.au/~asloane/plrg/reading/genPar03.pdf},
  year = 2003
}

Endnote record

%0 Report
%1 Mernik:2003
%A Mernik, Marjan
%A {\v C}repin{\v s}ek, Matej
%A Gerli{\v c}, Goran
%A {\v Z}umer, Viljem
%A Bryant, Barrett R.
%A Sprague, Alan
%D 2003
%K 
%T Learning Context-Free Grammars using an Evolutionary Approach
%U http://www.comp.mq.edu.au/~asloane/plrg/reading/genPar03.pdf
%X Machine learning of grammars finds many applications in syntactic pattern recognition, computational biology, natural language acquisition, etc. In this paper a new application of grammatical inference is suggested. Development of domain-specific languages is a hard problem for domain experts not versed in programming language design. We believe that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are the using genetic programming approach in grammatical inference. Grammar-specific heuristic operators and non-random construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by small examples where underlying context-free grammars are successfully inferred.

BibSonomy :: publication ::

Learning Context-Free Grammars using an Evolutionary Approach

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Abstract

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Endnote record