A technique for computer detection and correction of spelling errors
Communications of the ACM
Effective foreign word extration for Korean information retrieval
Information Processing and Management: an International Journal
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
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A hybrid back-transliteration system for Japanese
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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
Improving back-transliteration by combining information sources
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Machine transliteration survey
ACM Computing Surveys (CSUR)
English to persian transliteration
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
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Transliterating words and names from one language to another is a frequent and highly productive phenomenon. For example, English word cache is transliterated in Japanese as キャツシェ “kyasshu”. Transliteration is information losing since important distinctions are not always preserved in the process. Hence, automatically converting transliterated words back into their original form is a real challenge. Nonetheless, due to its wide applicability in MT and CLIR, it is an interesting problem from a practical point of view. In this paper, we demonstrate that back-transliteration accuracy can be improved by directly combining grapheme-based (i.e. spelling) and phoneme-based (i.e. pronunciation) information. Rather than producing back-transliterations based on grapheme and phoneme model independently and then interpolating the results, we propose a method of first combining the sets of allowed rewrites (i.e. edits) and then calculating the back-transliterations using the combined set. Evaluation on both Japanese and Chinese transliterations shows that direct combination increases robustness and positively affects back-transliteration accuracy.