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Extraction of Transliteration Pairs from Parallel Corpora Using a Statistical Transliteration Model by Chun-Jen Lee; Jason S. Chang; Jyh-Shing Roger Jang is a Computer Science article available to read on EtoBox.

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This paper describes a framework for modeling the machine transliteration problem. The parameters of the proposed model are automatically acquired through statistical learning from a bilingual proper name list. Unlike previous approaches, the model does not involve the use of either a pronunciation dictionary for converting source words into phonetic symbols or manually assigned phonetic similarity scores between source and target words. We also report how the model is applied to extract proper names and corresponding transliterations from parallel corpora. Experimental results show that the average rates of word and character precision are 93.8% and 97.8%, respectively.

Who reads Extraction of Transliteration Pairs from Parallel Corpora Using a Statistical Transliteration Model?

Author

Chun-Jen Lee; Jason S. Chang; Jyh-Shing Roger Jang

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0020-0255)

Published

2006

Language

EN

Field

Computer Science (Physical Sciences)