IEICE TRANSACTIONS on Information
Machine Learning Based English-to-Korean Transliteration Using Grapheme and Phoneme Information
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
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.7 pp.1737-1748
- Publication Date
- 2005/07/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.7.1737
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Keyword
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Jong-Hoon OH, Key-Sun CHOI, "Machine Learning Based English-to-Korean Transliteration Using Grapheme and Phoneme Information" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 7, pp. 1737-1748, July 2005, doi: 10.1093/ietisy/e88-d.7.1737.
Abstract: 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
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.7.1737/_p
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@ARTICLE{e88-d_7_1737,
author={Jong-Hoon OH, Key-Sun CHOI, },
journal={IEICE TRANSACTIONS on Information},
title={Machine Learning Based English-to-Korean Transliteration Using Grapheme and Phoneme Information},
year={2005},
volume={E88-D},
number={7},
pages={1737-1748},
abstract={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
keywords={},
doi={10.1093/ietisy/e88-d.7.1737},
ISSN={},
month={July},}
Copy
TY - JOUR
TI - Machine Learning Based English-to-Korean Transliteration Using Grapheme and Phoneme Information
T2 - IEICE TRANSACTIONS on Information
SP - 1737
EP - 1748
AU - Jong-Hoon OH
AU - Key-Sun CHOI
PY - 2005
DO - 10.1093/ietisy/e88-d.7.1737
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2005
AB - 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
ER -
English
