Statistical Machine Translation Using the Self-Organizing Map

Título

dc.contributor.author	López, Vivian
dc.contributor.author	Corchado Rodríguez, Juan Manuel
dc.contributor.author	De Paz, Juan F.
dc.contributor.author	Rodríguez González, Sara
dc.contributor.author	Bajo Pérez, Javier
dc.date.accessioned	2017-09-06T09:15:02Z
dc.date.available	2017-09-06T09:15:02Z
dc.date.issued	2010
dc.identifier.citation	Distributed Computing and Artificial Intelligence Advances in Intelligent and Soft Computing. Advances in Intelligent and Soft Computing. Volumen 79, pp. 131-138.
dc.identifier.isbn	978-3-642-14882-8 (Print) / 978-3-642-14883-5 (Online)
dc.identifier.issn	1867-5662 (Print) / 1867-5670 (Online)
dc.identifier.uri	http://hdl.handle.net/10366/134939
dc.description.abstract	The paper describes a contextual environment using the Self-Organizing Map, which can model a semantic agent (SOMAgent) that learns the correct meaning of a word used in context in order to deal with specific phenomena such as ambiguity, and to generate more precise alignments that can improve the first choice of the Statistical Machine Translation system giving linguistic knowledge.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Springer Science + Business Media
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	Statistical Machine Translation Using the Self-Organizing Map
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess