Document translation retrieval based on statistical machine translation techniques
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/27538
Título: | Document translation retrieval based on statistical machine translation techniques |
---|---|
Autor/es: | Sánchez-Martínez, Felipe | Carrasco, Rafael C. |
Grupo/s de investigación o GITE: | Transducens |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Machine translation | Statistical machine translation techniques | Document translation retrieval |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 20-may-2011 |
Editor: | Taylor & Francis |
Cita bibliográfica: | SÁNCHEZ-MARTÍNEZ, Felipe; CARRASCO, Rafael C. “Document translation retrieval based on statistical machine translation techniques”. Applied Artificial Intelligence. Vol. 25, Issue 5 (2011). ISSN 0883-9514, pp. 329-340 |
Resumen: | We compare different strategies to apply statistical machine translation techniques in order to retrieve documents that are a plausible translation of a given source document. Finding the translated version of a document is a relevant task; for example, when building a corpus of parallel texts that can help to create and evaluate new machine translation systems. In contrast to the traditional settings in cross-language information retrieval tasks, in this case both the source and the target text are long and, thus, the procedure used to select which words or phrases will be included in the query has a key effect on the retrieval performance. In the statistical approach explored here, both the probability of the translation and the relevance of the terms are taken into account in order to build an effective query. |
Patrocinador/es: | This work has been funded by the Spanish Ministry of Science and Innovation through project TIN2009-14009-C02-01. |
URI: | http://hdl.handle.net/10045/27538 |
ISSN: | 0883-9514 (Print) | 1087-6545 (Online) |
DOI: | 10.1080/08839514.2011.559906 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | This is an Author's Accepted Manuscript of an article published in Applied Artificial Intelligence, 25:329–340, 2011, Copyright © 2011 Taylor & Francis Group, LLC, available online at: http://www.tandfonline.com/10.1080/08839514.2011.559906. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1080/08839514.2011.559906 |
Aparece en las colecciones: | INV - TRANSDUCENS - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
sanchez-martinez11a-1.pdf | Versión revisada (acceso abierto) | 135,37 kB | Adobe PDF | Abrir Vista previa |
sanchez-martinez11a-1_final.pdf | Versión final (acceso restringido) | 83,84 kB | Adobe PDF | Abrir Solicitar una copia |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.