Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A maximum entropy approach to natural language processing
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Machine Learning
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
An English to Korean transliteration model of extended Markov window
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
English-to-Korean transliteration using multiple unbounded overlapping phoneme chunks
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Mining the Web to Create a Language Model for Mapping between English Names and Phrases and Japanese
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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
Mining translations of OOV terms from the web through cross-lingual query expansion
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Backward machine transliteration by learning phonetic similarity
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Mining key phrase translations from web corpora
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Translating names and technical terms in Arabic text
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
Improving back-transliteration by combining information sources
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised lexicon mining from parenthetical expressions in monolingual web pages
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Journal of Artificial Intelligence Research
Hi-index | 0.00 |
Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models - grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model - have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.