Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Foundations of statistical natural language processing
Foundations of statistical natural language processing
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Transliteration of proper names in cross-lingual information retrieval
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
Translating names and technical terms in Arabic text
Semitic '98 Proceedings of the Workshop on Computational Approaches to Semitic Languages
Machine transliteration survey
ACM Computing Surveys (CSUR)
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In natural language processing, information about a person's geographical origin is an important feature for name entity transliteration and question answering. We propose a language-independent name origin clustering and classification framework. Provided with a small amount of bilingual name translation pairs with labeled origins, we measure origin similarities based on the perplexities of name character language and translation models. We group similar origins into clusters, then train a Bayesian classifier with different features. It achieves 84% classification accuracy with source names only, and 91% with both source and target name pairs. We apply the origin clustering and classification technique to a name transliteration task. The cluster-specific transliteration model dramatically improves the transliteration accuracy from 3.8% to 55%, reducing the transliteration character error rate from 50.3 to 13.5. Adding more unlabeled name pairs to the cluster-specific name transliteration model further improves the transliteration accuracy.