A technique for computer detection and correction of spelling errors
Communications of the ACM
Computational Linguistics
An English-Korean transliteration model using pronunciation and contextual rules
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Backward machine transliteration by learning phonetic similarity
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Translating names and technical terms in Arabic text
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
An ensemble of transliteration models for information retrieval
Information Processing and Management: an International Journal
Direct orthographical mapping for machine transliteration
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A comparison of different machine transliteration models
Journal of Artificial Intelligence Research
Machine transliteration survey
ACM Computing Surveys (CSUR)
Improving machine transliteration performance by using multiple transliteration models
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Direct combination of spelling and pronunciation information for robust back-transliteration
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
An ensemble of grapheme and phoneme for machine transliteration
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
| Hi-index | 0.01 |
Transliterating words and names from one language to another is a frequent and highly productive phenomenon. Transliteration is information loosing since important distinctions are not preserved in the process. Hence, automatically converting transliterated words back into their original form is a real challenge. However, due to wide applicability in MT and CLIR, it is a computationally interesting problem. Previously proposed back-transliteration methods are based either on phoneme modeling or grapheme modeling across languages. In this paper, we propose a new method, combining the two models in order to enhance the back–transliterations of words transliterated in Japanese. Our experiments show that the resulting system outperforms single-model systems.