Automatic Translation of Multi-word Labels

Grzegorz M. Protaziuk , Marcin Kaczyński , Robert Bembenik

Abstract

Application of semantic resources often requires linking phrases expressed in a natural language to formally defined notions. In case of ontologies lexical layers may be used for that purpose. In the paper we propose an automatic machine translation method for translating multi-word labels from lexical layers of domain ontologies. In the method we take advantage of Wikipedia and dictionaries services available on the Internet in order to provide translations of thematic texts from a given area of interest. Experimental evaluation shows usefulness of the proposed method in translating specialized thematic dictionaries.
Author Grzegorz M. Protaziuk II
Grzegorz M. Protaziuk,,
- The Institute of Computer Science
, Marcin Kaczyński II
Marcin Kaczyński,,
- The Institute of Computer Science
, Robert Bembenik II
Robert Bembenik,,
- The Institute of Computer Science
Pages99-109
Publication size in sheets0.5
Book Ryżko Dominik Paweł, Gawrysiak Piotr, Kryszkiewicz Marzena, Rybiński Henryk (eds.): Machine Intelligence and Big Data in Industry, Studies in Big Data, vol. 19, 2016, Springer International Publishing Switzerland, ISBN 978-3-319-30314-7, [978-3-319-30315-4], 236 p., DOI:10.1007/978-3-319-30315-4 document.gif
Keywords in EnglishDomain label translation – Automatic translation – Wikipedia application
DOIDOI:10.1007/978-3-319-30315-4_9
URL http://link.springer.com/chapter/10.1007/978-3-319-30315-4_9
projectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Rybiński Henryk, , Phone: +48 22 234 7731, start date 18-05-2015, end date 30-11-2016, II/2015/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
File
01230089.pdf (file archived - login or check accessibility on faculty) 01230089.pdf 653.22 KB
Score (nominal)15
ScoreMinisterial score = 15.0, 27-03-2017, BookChapterSeriesAndMatConfByIndicator
Ministerial score (2013-2016) = 15.0, 27-03-2017, BookChapterSeriesAndMatConfByIndicator
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