Algorithms for Arabic name transliteration
IBM Journal of Research and Development
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical transliteration for english-arabic cross language information retrieval
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Computational Linguistics
Bitext maps and alignment via pattern recognition
Computational Linguistics
Automatic English-Chinese name transliteration for development of multilingual resources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Proper name translation in cross-language information retrieval
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
A pattern matching method for finding noun and proper noun translations from noisy parallel corpora
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Translating named entities using monolingual and bilingual resources
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Multipath translation lexicon induction via bridge languages
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Backward machine transliteration by learning phonetic similarity
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Translating–transliterating named entities for multilingual information access
Journal of the American Society for Information Science and Technology
An ensemble of transliteration models for information retrieval
Information Processing and Management: an International Journal
Stemming to improve translation lexicon creation form bitexts
Information Processing and Management: an International Journal
Named entity transliteration with comparable corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
Extraction of transliteration pairs from parallel corpora using a statistical transliteration model
Information Sciences: an International Journal
Probabilistic neural network based english-arabic sentence alignment
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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Proper nouns may be considered the most important query words in information retrieval. If the two languages use the same alphabet, the same proper nouns can be found in either language. However, if the two languages use different alphabets, the names must be transliterated. Short vowels are not usually marked on Arabic words in almost all Arabic documents (except very important documents like the Muslim and Christian holy books). Moreover, most Arabic words have a syllable consisting of a consonant-vowel combination (CV), which means that most Arabic words contain a short or long vowel between two successive consonant letters. That makes it difficult to create English-Arabic transliteration pairs, since some English letters may not be matched with any romanized Arabic letter. In the present study, we present different approaches for extraction of transliteration proper-noun pairs from parallel corpora based on different similarity measures between the English and romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the new approaches presented using two different English-Arabic parallel corpora. Most of our results outperform previously published results in terms of precision, recall, and F-Measure. © 2008 Wiley Periodicals, Inc.