The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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
Translating named entities using monolingual and bilingual resources
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
MultiNER '03 Proceedings of the ACL 2003 workshop on Multilingual and mixed-language named entity recognition - Volume 15
Named entity translation matching and learning: With application for mining unseen translations
ACM Transactions on Information Systems (TOIS)
Automated mining of names using parallel Hindi-English corpus
ALR7 Proceedings of the 7th Workshop on Asian Language Resources
Transliteration mining with phonetic conflation and iterative training
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Improved transliteration mining using graph reinforcement
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Transliteration mining using large training and test sets
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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In this article we present an automatic approach to extracting Hindi-English (H-E) Named Entity (NE) translingual equivalences from bilingual parallel corpora. In the absence of a Hindi NE tagger or H-E translation dictionary, this approach adapts a Chinese-English (C-E) surface string transliteration model for H-E NE extraction. The model is initially trained using automatically extracted C-E NE pairs, then iteratively updated based on newly extracted H-E NE pairs. For each English person and location NE in each sentence pair, this approach searches for its Hindi correspondence with minimum transliteration cost and constructs an H-E NE list from the bilingual corpus. Experiments show that this approach extracted 1000 H-E NE pairs with a precision of 91.8%.