dc.contributor.author | Bungum, Lars | |
dc.contributor.author | Gambäck, Björn | |
dc.date.accessioned | 2016-01-23T20:45:13Z | |
dc.date.accessioned | 2016-06-03T08:42:59Z | |
dc.date.available | 2016-01-23T20:45:13Z | |
dc.date.available | 2016-06-03T08:42:59Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Christiansen, Henning; Stojanovic, Isidora; Papadopoulos, George A. [Eds.] Modeling and Using Context: 9th International and Interdisciplinary Conference, CONTEXT 2015, Larnaca, Cyprus, November 2-6, 2015. Proceedings p. 201-213 Lecture Notes in Computer Science, Springer Science+Business Media B.V., 2015 | nb_NO |
dc.identifier.isbn | 978-3-319-25590-3 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/11250/2391293 | |
dc.description.abstract | Domain Adaptation in Machine Translation means to take a machine translation system that is restricted to work in a specific context and to enable the system to translate text from a different domain. The paper presents a two-step domain adaptation strategy, by first making use of unlabeled training material through an unsupervised algorithm, the Self-Organizing Map, to create auxiliary language models, and then to include these models dynamically in a machine translation pipeline | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer | nb_NO |
dc.relation.ispartofseries | LNCS; | |
dc.subject | (Statistical) Machine Translation – Domain adaptation – Self-Organizing Maps – Hierarchical clustering – Unstructured data | nb_NO |
dc.title | Multi-Domain Adapted Machine Translation Using Unsupervised Text Clustering | nb_NO |
dc.type | Chapter | nb_NO |
dc.date.updated | 2016-01-23T20:45:13Z | |
dc.description.version | acceptedVersion | |
dc.source.pagenumber | 201-213 | nb_NO |
dc.source.volume | 9405 | nb_NO |
dc.source.journal | Lecture Notes in Computer Science | nb_NO |
dc.identifier.doi | 10.1007/978-3-319-25591-0_15 | |
dc.identifier.cristin | 1320895 | |
dc.description.localcode | Author postprint - The original publication is available at www.springerlink.com | nb_NO |