Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
Machine Learning Based English-to-Korean Transliteration Using Grapheme and Phoneme Information Jong-Hoon OH Key-Sun CHOI Publication IEICE TRANSACTIONS on Information and Systems Vol.E88-D No.7 pp.1737-1748 Publication Date: 2005/07/01 Online ISSN: DOI: 10.1093/ietisy/e88-d.7.1737 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Natural Language Processing Keyword: machine transliteration, machine learning, information retrieval, machine translation, natural language processing, Full Text: PDF(1.4MB)>>
Summary: Machine transliteration is an automatic method to generate characters or words in one alphabetical system for the corresponding characters in another alphabetical system. Machine transliteration can play an important role in natural language application such as information retrieval and machine translation, especially for handling proper nouns and technical terms. The previous works focus on either a grapheme-based or phoneme-based method. However, transliteration is an orthographical and phonetic converting process. Therefore, both grapheme and phoneme information should be considered in machine transliteration. In this paper, we propose a grapheme and phoneme-based transliteration model and compare it with previous grapheme-based and phoneme-based models using several machine learning techniques. Our method shows about 13  78% performance improvement. | ||||||||||
| ||||||||||
