IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Linguistics
An English to Korean transliteration model of extended Markov window
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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
Weakly supervised named entity transliteration and discovery from multilingual comparable corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Named entity transliteration and discovery from multilingual comparable corpora
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Active sample selection for named entity transliteration
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Incremental integer linear programming for non-projective dependency parsing
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Unsupervised named entity transliteration using temporal and phonetic correlation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transliteration as constrained optimization
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning and inference with constraints
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Learning better transliterations
Proceedings of the 18th ACM conference on Information and knowledge management
Exploiting bilingual information to improve web search
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Discriminative learning over constrained latent representations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Language independent transliteration mining system using finite state automata framework
NEWS '10 Proceedings of the 2010 Named Entities Workshop
Modeling relations and their mentions without labeled text
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Confidence driven unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
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This paper introduces a novel unsupervised constraint-driven learning algorithm for identifying named-entity (NE) transliterations in bilingual corpora. The proposed method does not require any annotated data or aligned corpora. Instead, it is bootstrapped using a simple resource -- a romanization table. We show that this resource, when used in conjunction with constraints, can efficiently identify transliteration pairs. We evaluate the proposed method on transliterating English NEs to three different languages - Chinese, Russian and Hebrew. Our experiments show that constraint driven learning can significantly outperform existing unsupervised models and achieve competitive results to existing supervised models.