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Campo DCValorLenguaje
dc.creatorTurchi,Marco-
dc.creatorEhrmann,Maud-
dc.date2011-06-01-
dc.date.accessioned2012-07-31T17:22:33Z-
dc.date.available2012-07-31T17:22:33Z-
dc.date.issued2012-07-31-
dc.identifierhttp://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-90442011000100005-
dc.identifier.urihttp://repositoriodigital.academica.mx/jspui/handle/987654321/83060-
dc.descriptionTranslation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efflciently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.-
dc.formattext/html-
dc.languageen-
dc.publisherPolibits-
dc.subjectMachine translation-
dc.subjectknowledge-
dc.subjectmorphological resources-
dc.titleKnowledge Expansion of a Statistical Machine Translation System using Morphological Resources-
dc.typejournal article-
Aparece en las Colecciones:Polibits

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