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      Learning translation templates for closely related languages

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      Author
      Altıntaş, Kemal
      Güvenir, H. Altay
      Date
      2003
      Source Title
      Knowledge-Based Intelligent Information and Engineering Systems
      Print ISSN
      0302-9743
      Publisher
      Springer, Berlin, Heidelberg
      Volume
      2773
      Pages
      756 - 762
      Language
      English
      Type
      Conference Paper
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      Abstract
      Many researchers have worked on example-based machine translation and different techniques have been investigated in the area. In literature, a method of using translation templates learned from bilingual example pairs was proposed. The paper investigates the possibility of applying the same idea for close languages where word order is preserved. In addition to applying the original algorithm for example pairs, we believe that the similarities between the translated sentences may always be learned as atomic translations. Since the word order is almost always preserved, there is no need to have any previous knowledge to identify the corresponding differences. The paper concludes that applying this method for close languages may improve the performance of the system.
      Keywords
      Example-Based Machine Translation (EBMT)
      Language Processing
      Machine Translation
      Operational Systems
      Example Based Machine Translations
      Original Algorithms
      Translation Templates
      Word Orders
      Algorithms
      Computer Science
      Linguistics
      Neural Networks
      Semantics
      Computational Linguistics
      Knowledge Based Systems
      Computer Programming Languages
      Translation (languages)
      Permalink
      http://hdl.handle.net/11693/27511
      Published Version (Please cite this version)
      https://doi.org/10.1007/978-3-540-45224-9_102
      https://doi.org/10.1007/978-3-540-45224-9
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      • Department of Computer Engineering 1368
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