Evolutionary TBL template generation

Detalhes bibliográficos
Autor(a) principal: Milidiú,Ruy Luiz
Data de Publicação: 2007
Outros Autores: Duarte,Julio Cesar, Santos,Cícero Nogueira dos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Journal of the Brazilian Computer Society
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002007000400004
Resumo: Transformation Based Learning (TBL) is a Machine Learning technique frequently used in some Natural Language Processing (NLP) tasks. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template generation process. Additionally, we report our findings on five experiments with useful NLP tasks. We observe that our approach provides template sets with a mean loss of performance of 0.5% when compared to human built templates
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spelling Evolutionary TBL template generationMachine LearningGenetic AlgorithmsTransformation Error-Driven Based LearningTransformation Based Learning (TBL) is a Machine Learning technique frequently used in some Natural Language Processing (NLP) tasks. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template generation process. Additionally, we report our findings on five experiments with useful NLP tasks. We observe that our approach provides template sets with a mean loss of performance of 0.5% when compared to human built templatesSociedade Brasileira de Computação2007-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002007000400004Journal of the Brazilian Computer Society v.13 n.4 2007reponame:Journal of the Brazilian Computer Societyinstname:Sociedade Brasileira de Computação (SBC)instacron:UFRGS10.1007/BF03194255info:eu-repo/semantics/openAccessMilidiú,Ruy LuizDuarte,Julio CesarSantos,Cícero Nogueira doseng2010-05-24T00:00:00Zoai:scielo:S0104-65002007000400004Revistahttps://journal-bcs.springeropen.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpjbcs@icmc.sc.usp.br1678-48040104-6500opendoar:2010-05-24T00:00Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC)false
dc.title.none.fl_str_mv Evolutionary TBL template generation
title Evolutionary TBL template generation
spellingShingle Evolutionary TBL template generation
Milidiú,Ruy Luiz
Machine Learning
Genetic Algorithms
Transformation Error-Driven Based Learning
title_short Evolutionary TBL template generation
title_full Evolutionary TBL template generation
title_fullStr Evolutionary TBL template generation
title_full_unstemmed Evolutionary TBL template generation
title_sort Evolutionary TBL template generation
author Milidiú,Ruy Luiz
author_facet Milidiú,Ruy Luiz
Duarte,Julio Cesar
Santos,Cícero Nogueira dos
author_role author
author2 Duarte,Julio Cesar
Santos,Cícero Nogueira dos
author2_role author
author
dc.contributor.author.fl_str_mv Milidiú,Ruy Luiz
Duarte,Julio Cesar
Santos,Cícero Nogueira dos
dc.subject.por.fl_str_mv Machine Learning
Genetic Algorithms
Transformation Error-Driven Based Learning
topic Machine Learning
Genetic Algorithms
Transformation Error-Driven Based Learning
description Transformation Based Learning (TBL) is a Machine Learning technique frequently used in some Natural Language Processing (NLP) tasks. TBL uses rule templates to identify error-correcting patterns. A critical requirement in TBL is the availability of a problem domain expert to build these rule templates. In this work, we propose an evolutionary approach based on Genetic Algorithms to automatically implement the template generation process. Additionally, we report our findings on five experiments with useful NLP tasks. We observe that our approach provides template sets with a mean loss of performance of 0.5% when compared to human built templates
publishDate 2007
dc.date.none.fl_str_mv 2007-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002007000400004
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-65002007000400004
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1007/BF03194255
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Computação
publisher.none.fl_str_mv Sociedade Brasileira de Computação
dc.source.none.fl_str_mv Journal of the Brazilian Computer Society v.13 n.4 2007
reponame:Journal of the Brazilian Computer Society
instname:Sociedade Brasileira de Computação (SBC)
instacron:UFRGS
instname_str Sociedade Brasileira de Computação (SBC)
instacron_str UFRGS
institution UFRGS
reponame_str Journal of the Brazilian Computer Society
collection Journal of the Brazilian Computer Society
repository.name.fl_str_mv Journal of the Brazilian Computer Society - Sociedade Brasileira de Computação (SBC)
repository.mail.fl_str_mv jbcs@icmc.sc.usp.br
_version_ 1754734669954285568

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