Translation Rules and ANN based model for English to Urdu Machine Translation
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | |
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/345 |
Resumo: | In this paper we discuss the working of our English to Urdu Machine Translation (MT) system. We used feed-forward back-propagation artificial neural network for the selection of Urdu words/tokens (such as verb, noun/pronoun etc.) and translation rules for grammar structure equivalent to English words/tokens and grammar structure rules respectively. As English is SVO class language while Urdu is SOV class language so grammar structure transfer is main task in English-Urdu machine translation problem. Our system is able to translate sentences having gerund, having infinitives (maximum two), having prepositions and prepositional objects (maximum three), direct object, indirect object etc. Neural network works as the knowledge base for linguistic rules and bilingual dictionary. Bilingual dictionary not only stores the meaning of English word in Urdu but also stores linguistic features attached to the word. The output of our system is presented in Romanized Urdu. The n-gram blue score achieved by the system is 0.6954; METEOR score achieved is 0.8583 and F-score of 0.8650. |
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INFOCOMP: Jornal de Ciência da Computação |
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Translation Rules and ANN based model for English to Urdu Machine TranslationNeural networkback-propagationrule based translationEnglishUrdumachine translation systemArtificial IntelligenceIn this paper we discuss the working of our English to Urdu Machine Translation (MT) system. We used feed-forward back-propagation artificial neural network for the selection of Urdu words/tokens (such as verb, noun/pronoun etc.) and translation rules for grammar structure equivalent to English words/tokens and grammar structure rules respectively. As English is SVO class language while Urdu is SOV class language so grammar structure transfer is main task in English-Urdu machine translation problem. Our system is able to translate sentences having gerund, having infinitives (maximum two), having prepositions and prepositional objects (maximum three), direct object, indirect object etc. Neural network works as the knowledge base for linguistic rules and bilingual dictionary. Bilingual dictionary not only stores the meaning of English word in Urdu but also stores linguistic features attached to the word. The output of our system is presented in Romanized Urdu. The n-gram blue score achieved by the system is 0.6954; METEOR score achieved is 0.8583 and F-score of 0.8650.Editora da UFLA2011-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/345INFOCOMP Journal of Computer Science; Vol. 10 No. 3 (2011): September, 2011; 36-471982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/345/329Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessKhan, ShahnawazMishra, R. B.2015-07-29T12:20:43Zoai:infocomp.dcc.ufla.br:article/345Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:33.219846INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
Translation Rules and ANN based model for English to Urdu Machine Translation |
title |
Translation Rules and ANN based model for English to Urdu Machine Translation |
spellingShingle |
Translation Rules and ANN based model for English to Urdu Machine Translation Khan, Shahnawaz Neural network back-propagation rule based translation English Urdu machine translation system Artificial Intelligence |
title_short |
Translation Rules and ANN based model for English to Urdu Machine Translation |
title_full |
Translation Rules and ANN based model for English to Urdu Machine Translation |
title_fullStr |
Translation Rules and ANN based model for English to Urdu Machine Translation |
title_full_unstemmed |
Translation Rules and ANN based model for English to Urdu Machine Translation |
title_sort |
Translation Rules and ANN based model for English to Urdu Machine Translation |
author |
Khan, Shahnawaz |
author_facet |
Khan, Shahnawaz Mishra, R. B. |
author_role |
author |
author2 |
Mishra, R. B. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Khan, Shahnawaz Mishra, R. B. |
dc.subject.por.fl_str_mv |
Neural network back-propagation rule based translation English Urdu machine translation system Artificial Intelligence |
topic |
Neural network back-propagation rule based translation English Urdu machine translation system Artificial Intelligence |
description |
In this paper we discuss the working of our English to Urdu Machine Translation (MT) system. We used feed-forward back-propagation artificial neural network for the selection of Urdu words/tokens (such as verb, noun/pronoun etc.) and translation rules for grammar structure equivalent to English words/tokens and grammar structure rules respectively. As English is SVO class language while Urdu is SOV class language so grammar structure transfer is main task in English-Urdu machine translation problem. Our system is able to translate sentences having gerund, having infinitives (maximum two), having prepositions and prepositional objects (maximum three), direct object, indirect object etc. Neural network works as the knowledge base for linguistic rules and bilingual dictionary. Bilingual dictionary not only stores the meaning of English word in Urdu but also stores linguistic features attached to the word. The output of our system is presented in Romanized Urdu. The n-gram blue score achieved by the system is 0.6954; METEOR score achieved is 0.8583 and F-score of 0.8650. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-09-01 |
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/345 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/345 |
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/345/329 |
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. 10 No. 3 (2011): September, 2011; 36-47 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_ |
1799874741391065088 |