Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10609/151316
Título : A system for terminology extraction and translation equivalent detection in real time Efficient use of statistical machine translation phrase tables
Autoría: Oliver, Antoni  
Citación : Oliver, A. [Antoni]. (2017). A system for terminology extraction and translation equivalent detection in real time. Efficient use of Statistical Machine Translation phrase tables. Machine Translation, 31(3), 147-161. doi: 10.1007/s10590-017-9201-7
Resumen : In this paper we present a system for automatic terminology extraction and automatic detection of the equivalent terms in the target language to be used alongside a computer assisted translation (CAT) tool that provides term candidates and their translations in an automatic way each time the translator goes from one segment to the next one. The system uses several sources of information: the text from the segment being translated and from the whole translation project, the translation memories assigned to the project and a translation phrase table from a statistical machine translation system. It also uses the terminological database assigned to the project in order to avoid presenting already known terms. The use of translation phrase tables allows us to use very large parallel corpora in a very efficient way. We have used Moses to calculate and to consult the translation phrase tables. The program is written in Python and it can be used with any CAT tool. In our experiments we have used OmegaT, a well-known open source CAT tool. Evaluation results for English–Spanish and for three subjects (politics, finance, and medicine) are presented.
Palabras clave : automatic terminology extraction
translation equivalent detection
translation memories
SMT translation models
computer assisted translation
DOI: https://doi.org/10.1007/s10590-017-9201-7
Tipo de documento: info:eu-repo/semantics/article
Versión del documento: info:eu-repo/semantics/submittedVersion
Fecha de publicación : 17-oct-2017
Licencia de publicación: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
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