Improving Machine Transliteration Performance by Using Multiple Transliteration Models

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 735
  • Download : 1
Machine transliteration has received significant attention as a supporting tool for machine translation and cross-language information retrieval. During the last decade, four kinds of transliteration model have been studied — grapheme-based model, phoneme-based model, hybrid model, and correspondence-based model. These models are classified in terms of the information sources for transliteration or the units to be transliterated — source graphemes, source phonemes, both source graphemes and source phonemes, and the correspondence between source graphemes and phonemes, respectively. Although each transliteration model has shown relatively good performance, one model alone has limitations on handling complex transliteration behaviors. To address the problem, we combined different transliteration models with a “generating transliterations followed by their validation” strategy. The strategy makes it possible to consider complex transliteration behaviors using the strengths of each model and to improve transliteration performance by validating transliterations. Our method makes use of web-based and transliteration model-based validation for transliteration validation. Experiments showed that our method outperforms both the individual transliteration models and previous work.
Publisher
Springer Verlag (Germany)
Issue Date
2006-12
Citation

Lecture Notes in Computer Science, Vol. 4285, P. 85 - 96

ISSN
0302-9743
DOI
10.1007/11940098_9
URI
http://hdl.handle.net/10203/3575
Appears in Collection
CS-Journal Papers(저널논문)

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0