Computational Linguistics
Automatic English-Chinese name transliteration for development of multilingual resources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
An English to Korean transliteration model of extended Markov window
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
An English-Korean transliteration model using pronunciation and contextual rules
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Translating named entities using monolingual and bilingual resources
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
Transliteration of proper names in cross-lingual information retrieval
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
Phoneme-Based transliteration of foreign names for OOV problem
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Improving back-transliteration by combining information sources
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Multilingual modeling of cross-lingual spelling variants
Information Retrieval
Named entity transcription with pair n-gram models
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Transliteration for Resource-Scarce Languages
ACM Transactions on Asian Language Information Processing (TALIP)
Machine transliteration survey
ACM Computing Surveys (CSUR)
Machine transliteration: leveraging on third languages
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Statistical machine translation enhancements through linguistic levels: A survey
ACM Computing Surveys (CSUR)
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Machine transliteration/back-transliteration plays an important role in many multilingual speech and language applications. In this paper, a novel framework for machine transliteration/back-transliteration that allows us to carry out direct orthographical mapping (DOM) between two different languages is presented. Under this framework, a joint source-channel transliteration model, also called n-gram transliteration model (n-gram TM), is further proposed to model the transliteration process. We evaluate the proposed methods through several transliteration/back-transliteration experiments for English/Chinese and English/Japanese language pairs. Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the transliteration accuracy significantly.