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Improving Back-Transliteration by Combining Information Sources

  • Slaven Bilac
  • Hozumi Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3248)

Abstract

Transliterating words and names from one language to another is a frequent and highly productive phenomenon. Transliteration is information loosing since important distinctions are not preserved in the process. Hence, automatically converting transliterated words back into their original form is a real challenge. However, due to wide applicability in MT and CLIR, it is a computationally interesting problem. Previously proposed back-transliteration methods are based either on phoneme modeling or grapheme modeling across languages. In this paper, we propose a new method, combining the two models in order to enhance the back–transliterations of words transliterated in Japanese. Our experiments show that the resulting system outperforms single-model systems.

Keywords

Machine Translation English Word Input String English Alphabet Electronic Dictionary 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Slaven Bilac
    • 1
  • Hozumi Tanaka
    • 1
  1. 1.Department of Computer ScienceTokyo Institute of TechnologyTokyo

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