A Hybrid Learning for Named Entity Recognition Systems
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/243 |
Resumo: | This paper presents a hybrid method using machine learning approach for Named Entity Recognition (NER). A system built based on this method is able to achieve reasonable performance with minimal training data and gazetteers. The hybrid machine learning approach differs from previous machine learning-based systems in that it uses Maximum Entropy Model (MEM) and Hidden Markov Model (HMM) successively. We report on the performance of our proposed NER system using British National Corpus (BNC). In the recognition process, we first use MEM to identify the named entities in the corpus by imposing some temporary tagging as references. The MEM walkthrough can be regarded as a training process for HMM, as we then use HMM for the final tagging. We show that with enough training data and appropriate error correction mechanism, this approach can achieve higher precision and recall than using a single statistical model. We conclude with our experimental results that indicate the flexibility of our system in different domains. |
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INFOCOMP: Jornal de Ciência da Computação |
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A Hybrid Learning for Named Entity Recognition SystemsMachine learningnamed entity recognitiontaggingThis paper presents a hybrid method using machine learning approach for Named Entity Recognition (NER). A system built based on this method is able to achieve reasonable performance with minimal training data and gazetteers. The hybrid machine learning approach differs from previous machine learning-based systems in that it uses Maximum Entropy Model (MEM) and Hidden Markov Model (HMM) successively. We report on the performance of our proposed NER system using British National Corpus (BNC). In the recognition process, we first use MEM to identify the named entities in the corpus by imposing some temporary tagging as references. The MEM walkthrough can be regarded as a training process for HMM, as we then use HMM for the final tagging. We show that with enough training data and appropriate error correction mechanism, this approach can achieve higher precision and recall than using a single statistical model. We conclude with our experimental results that indicate the flexibility of our system in different domains.Editora da UFLA2008-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/243INFOCOMP Journal of Computer Science; Vol. 7 No. 4 (2008): December, 2008; 92-981982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/243/228Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessChiong, Raymond2015-07-01T12:39:26Zoai:infocomp.dcc.ufla.br:article/243Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:26.893298INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
A Hybrid Learning for Named Entity Recognition Systems |
title |
A Hybrid Learning for Named Entity Recognition Systems |
spellingShingle |
A Hybrid Learning for Named Entity Recognition Systems Chiong, Raymond Machine learning named entity recognition tagging |
title_short |
A Hybrid Learning for Named Entity Recognition Systems |
title_full |
A Hybrid Learning for Named Entity Recognition Systems |
title_fullStr |
A Hybrid Learning for Named Entity Recognition Systems |
title_full_unstemmed |
A Hybrid Learning for Named Entity Recognition Systems |
title_sort |
A Hybrid Learning for Named Entity Recognition Systems |
author |
Chiong, Raymond |
author_facet |
Chiong, Raymond |
author_role |
author |
dc.contributor.author.fl_str_mv |
Chiong, Raymond |
dc.subject.por.fl_str_mv |
Machine learning named entity recognition tagging |
topic |
Machine learning named entity recognition tagging |
description |
This paper presents a hybrid method using machine learning approach for Named Entity Recognition (NER). A system built based on this method is able to achieve reasonable performance with minimal training data and gazetteers. The hybrid machine learning approach differs from previous machine learning-based systems in that it uses Maximum Entropy Model (MEM) and Hidden Markov Model (HMM) successively. We report on the performance of our proposed NER system using British National Corpus (BNC). In the recognition process, we first use MEM to identify the named entities in the corpus by imposing some temporary tagging as references. The MEM walkthrough can be regarded as a training process for HMM, as we then use HMM for the final tagging. We show that with enough training data and appropriate error correction mechanism, this approach can achieve higher precision and recall than using a single statistical model. We conclude with our experimental results that indicate the flexibility of our system in different domains. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/243 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/243 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/243/228 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 7 No. 4 (2008): December, 2008; 92-98 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874740855242752 |