International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Discriminative Models for Automatic Acquisition of Translation Equivalences

  • Zhang, Chun-Xiang (School of Computer Science and Technology, Harbin Institute of Technology) ;
  • Li, Sheng (School of Computer Science and Technology, Harbin Institute of Technology) ;
  • Zhao, Tie-Jun (School of Computer Science and Technology, Harbin Institute of Technology)
  • Published : 2007.02.28
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Abstract

Translation equivalence is very important for bilingual lexicography, machine translation system and cross-lingual information retrieval. Extraction of equivalences from bilingual sentence pairs belongs to data mining problem. In this paper, discriminative learning methods are employed to filter translation equivalences. Discriminative features including translation literality, phrase alignment probability, and phrase length ratio are used to evaluate equivalences. 1000 equivalences randomly selected are filtered and then evaluated. Experimental results indicate that its precision is 87.8% and recall is 89.8% for support vector machine.

Keywords

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