Phrase-based Machine Translation based on Text Mining and Statistical Language Modeling Techniques
Identifieur interne : 002506 ( Main/Exploration ); précédent : 002505; suivant : 002507Phrase-based Machine Translation based on Text Mining and Statistical Language Modeling Techniques
Auteurs : Latiri Chiraz [France] ; Kamel Smaili [France] ; Caroline Lavecchia [France] ; Cyrine Nasri [France] ; David Langlois [France]Source :
- International Journal of Computational Linguistics and Applications [ 0976-0962 ] ; 2011.
English descriptors
- mix :
Abstract
In this paper, we introduce two new methods dedicated to phrase based machine translation. Both are based on mining a parallel corpus in order to nd out the couples of linguistic units which are translation of each other. The presented methods do not rely on any alignment in contrast to what is done usually by the statistical machine translation community. Each of them proposes a complete translation table containing translations of single words and phrases. The rst method is inspired from the well-known trigger language model while the second one is inspired from the association rules mining technique. All experiments ar e conducted on a large part of EUROPARL corpus and highlight the utility of both proposed approaches.
Url:
Affiliations:
- France
- Auvergne-Rhône-Alpes, Grand Est, Lorraine (région), Rhône-Alpes
- Grenoble, Metz, Nancy
- Université Joseph Fourier, Université Pierre-Mendès-France, Université de Grenoble, Université de Lorraine
Links toward previous steps (curation, corpus...)
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- to stream Hal, to step Curation: 003B82
- to stream Hal, to step Checkpoint: 002025
- to stream Main, to step Merge: 002898
- to stream Main, to step Curation: 002506
Le document en format XML
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BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno><idno type="IdUnivLorraine">[UL]100--</idno><orgName>Université de Lorraine</orgName><orgName type="acronym">UL</orgName><date type="start">2012-01-01</date><desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.univ-lorraine.fr/</ref></desc></org></tutelle><tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="IdRef">02636817X</idno><idno type="ISNI">0000000122597504</idno><orgName>Centre National de la Recherche Scientifique</orgName><orgName type="acronym">CNRS</orgName><date type="start">1939-10-19</date><desc><address><country key="FR"></country></address><ref type="url">http://www.cnrs.fr/</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><settlement type="city">Metz</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université de Lorraine</orgName></affiliation></author></analytic><series><title level="j">International Journal of Computational Linguistics and Applications</title><idno type="ISSN">0976-0962</idno><imprint><date type="datePub">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term> Bilingual corpora.</term><term> Inter-lingual association rules</term><term> Inter-lingual triggers</term><term> Sequence mining</term><term>Statistical machine translation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In this paper, we introduce two new methods dedicated to phrase based machine translation. Both are based on mining a parallel corpus in order to nd out the couples of linguistic units which are translation of each other. The presented methods do not rely on any alignment in contrast to what is done usually by the statistical machine translation community. Each of them proposes a complete translation table containing translations of single words and phrases. The rst method is inspired from the well-known trigger language model while the second one is inspired from the association rules mining technique. All experiments ar e conducted on a large part of EUROPARL corpus and highlight the utility of both proposed approaches.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Auvergne-Rhône-Alpes</li><li>Grand Est</li><li>Lorraine (région)</li><li>Rhône-Alpes</li></region><settlement><li>Grenoble</li><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université Joseph Fourier</li><li>Université Pierre-Mendès-France</li><li>Université de Grenoble</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Auvergne-Rhône-Alpes"><name sortKey="Chiraz, Latiri" sort="Chiraz, Latiri" uniqKey="Chiraz L" first="Latiri" last="Chiraz">Latiri Chiraz</name></region><name sortKey="Langlois, David" sort="Langlois, David" uniqKey="Langlois D" first="David" last="Langlois">David Langlois</name><name sortKey="Lavecchia, Caroline" sort="Lavecchia, Caroline" uniqKey="Lavecchia C" first="Caroline" last="Lavecchia">Caroline Lavecchia</name><name sortKey="Nasri, Cyrine" sort="Nasri, Cyrine" uniqKey="Nasri C" first="Cyrine" last="Nasri">Cyrine Nasri</name><name sortKey="Smaili, Kamel" sort="Smaili, Kamel" uniqKey="Smaili K" first="Kamel" last="Smaili">Kamel Smaili</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Exploration
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