The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Cross-training: learning probabilistic mappings between topics
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational Linguistics
Iterative cross-training: An algorithm for learning from unlabeled Web pages
International Journal of Intelligent Systems - Intelligent Technologies
A joint source-channel model for machine transliteration
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Creating multilingual translation lexicons with regional variations using web corpora
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
A phonetic similarity model for automatic extraction of transliteration pairs
ACM Transactions on Asian Language Information Processing (TALIP)
An ensemble of grapheme and phoneme for machine transliteration
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Machine transliteration survey
ACM Computing Surveys (CSUR)
Learning regional transliteration variants
Information Processing and Management: an International Journal
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This paper proposes a method to harvest regional transliteration variants with guided search. We first study how to incorporate transliteration knowledge into query formulation so as to significantly increase the chance of desired transliteration returns. Then, we study a cross-training algorithm, which explores valuable information across different regional corpora for the learning of transliteration models to in turn improve the overall extraction performance. The experimental results show that the proposed method not only effectively harvests a lexicon of regional transliteration variants but also mitigates the need of manual data labeling for transliteration modeling. We also conduct an inquiry into the underlying characteristics of regional transliterations that motivate the cross-training algorithm.