Approximate string-matching with q-grams and maximal matches
Theoretical Computer Science - Selected papers of the Combinatorial Pattern Matching School
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Statistical transliteration for english-arabic cross language information retrieval
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Computational Linguistics
An improved error model for noisy channel spelling correction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Adaptive Name Matching in Information Integration
IEEE Intelligent Systems
Multilingual modeling of cross-lingual spelling variants
Information Retrieval
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning transliteration lexicons from the web
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Multilingual and cross-lingual news topic tracking
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Extraction of transliteration pairs from parallel corpora using a statistical transliteration model
Information Sciences: an International Journal
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Any cross-language processing application has to first tackle the problem of transliteration when facing a language using another script. The first solution consists of using existing transliteration tools, but these tools are not usually suitable for all purposes. For some specific script pairs they do not even exist. Our aim is to discriminate transliterations across different scripts in a unified way using a learning method that builds a transliteration model out of a set of transliterated proper names. We compare two strings using an algorithm that builds a Levenshtein edit distance using n-grams costs. The evaluations carried out show that our similarity measure is accurate.