About: Pattern REcognition-based Statistically Enhanced MT

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Projekt, within Data Space : linked.opendata.cz associated with source document(s)

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Description	This proposal describes PRESEMT, a flexible and adaptable MT system, based on a language-independent method, whose principles ensure easy portability to new language pairs. This method attempts to overcome well-known problems of other MT approaches, e.g. bilingual corpora compilation or creation of new rules per language pair. PRESEMT will address the issue of effectively managing multilingual content and is expected to suggest a language-independent machine-learning-based methodology. The key aspects of PRESEMT involve syntactic phrase-based modelling, pattern recognition approaches (such as extended clustering or neural networks) or game theory techniques towards the development of a language-independent analysis, evolutionary algorithms for system optimisation. It is intended to be of a hybrid nature, combining linguistic processing with the positive aspects of corpus-driven approaches, such as SMT and EBMT. In order for PRESEMT to be easily amenable to new language pairs, relatively inexp (en) This proposal describes PRESEMT, a flexible and adaptable MT system, based on a language-independent method, whose principles ensure easy portability to new language pairs. This method attempts to overcome well-known problems of other MT approaches, e.g. bilingual corpora compilation or creation of new rules per language pair. PRESEMT will address the issue of effectively managing multilingual content and is expected to suggest a language-independent machine-learning-based methodology. The key aspects of PRESEMT involve syntactic phrase-based modelling, pattern recognition approaches (such as extended clustering or neural networks) or game theory techniques towards the development of a language-independent analysis, evolutionary algorithms for system optimisation. It is intended to be of a hybrid nature, combining linguistic processing with the positive aspects of corpus-driven approaches, such as SMT and EBMT. In order for PRESEMT to be easily amenable to new language pairs, relatively inexp (cs)
Title	Pattern REcognition-based Statistically Enhanced MT (en) Pattern REcognition-based Statistically Enhanced MT (cs)
http://linked.open...vai/cislo-smlouvy	http://linked.opendata.cz/resource/domain/vavai/cep/smlouva/1090/2011-321
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http://linked.open...avai/druh-souteze	http://linked.opendata.cz/resource/domain/vavai/cep/druh-souteze/RP
http://linked.open...domain/vavai/faze	http://linked.opendata.cz/resource/domain/vavai/cep/faze/54675389
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http://linked.open...n/vavai/kategorie	http://linked.opendata.cz/resource/domain/vavai/cep/kategorie/2
http://linked.open...vai/klicova-slova	machine translation; pattern recognition; SMT; language-independen method; syntactic phrase-based modelling; evolutionary algorithms (en)
http://linked.open...avai/konec-reseni	2012-12-31 (xsd:date)
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http://linked.open...avai/start-reseni	2010-01-01 (xsd:date)
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is http://linked.open...ain/vavai/projekt of	http://linked.opendata.cz/resource/domain/vavai/cep/ucast/2010/7E10057/orjk-14330 http://linked.opendata.cz/resource/domain/vavai/cep/ucast/2011/7E10057/orjk-14330 http://linked.opendata.cz/resource/domain/vavai/cep/ucast/2012/7E10057/orjk-14330 http://linked.opendata.cz/resource/domain/vavai/cep/ucast/2013/7E10057/orjk-14330

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