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The Scientific World Journal
Volume 2014, Article ID 745485, 10 pages
http://dx.doi.org/10.1155/2014/745485
Research Article

A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation

Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau, China

Received 30 August 2013; Accepted 4 December 2013; Published 10 February 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Longyue Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Lars Bungum, and Björn Gambäck, “Multi-domain adapted machine translation using unsupervised text clustering,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9405, pp. 201–213, 2015. View at Publisher · View at Google Scholar
  • Ana Luisa Varani Leal, and Siyou Liu, “Analysis of temporal adverbial phrases for Portuguese-Chinese machine translation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9727, pp. 62–73, 2016. View at Publisher · View at Google Scholar
  • Mirjam Sepesy Maučec, and Janez Brest, “Slavic languages in phrase-based statistical machine translation: a survey,” Artificial Intelligence Review, 2017. View at Publisher · View at Google Scholar
  • Krzysztof Wołk, and Agnieszka Wołk, “Augmenting SMT with generated pseudo-parallel corpora from monolingual news resources,” Advances in Intelligent Systems and Computing, vol. 569, pp. 308–316, 2017. View at Publisher · View at Google Scholar
  • Krzysztof Wołk, “Mixing Textual Data Selection Methods for Improved In-Domain Data Adaptation,” Trends and Advances in Information Systems and Technologies, vol. 746, pp. 367–377, 2018. View at Publisher · View at Google Scholar