Phonetic string matching: lessons from information retrieval
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Statistical transliteration for english-arabic cross language information retrieval
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Word re-ordering and DP-based search in statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Machine transliteration of names in Arabic text
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Report of NEWS 2009 machine transliteration shared task
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Language independent transliteration mining system using finite state automata framework
NEWS '10 Proceedings of the 2010 Named Entities Workshop
A method for generating rules for cross-lingual transliteration
Automatic Documentation and Mathematical Linguistics
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Everyday the newswire introduce events from all over the world, highlighting new names of persons, locations and organizations with different origins. These names appear as Out of Vocabulary (OOV) words for Machine translation, cross lingual information retrieval, and many other NLP applications. One way to deal with OOV words is to transliterate the unknown words, that is, to render them in the orthography of the second language. We introduce a statistical approach for transliteration only using the bilingual resources released in the shared task and without any previous knowledge of the target languages. Mapping the Transliteration problem to the Machine Translation problem, we make use of the phrase based SMT approach and apply it on substrings of names. In the English to Russian task, we report ACC (Accuracy in top-1) of 0.545, Mean F-score of 0.917, and MRR (Mean Reciprocal Rank) of 0.596. Due to time constraints, we made a single experiment in the English to Chinese task, reporting ACC, Mean F-score, and MRR of 0.411, 0.737, and 0.464 respectively. Finally, it is worth mentioning that the system is language independent since the author is not aware of either languages used in the experiments.