Improved Named Entity Translation and Bilingual Named Entity Extraction
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Translating named entities using monolingual and bilingual resources
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Extracting named entity translingual equivalence with limited resources
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Cluster-specific named entity transliteration
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HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
On jointly recognizing and aligning bilingual named entities
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Acquisition of translation knowledge of syntactically ambiguous named entity
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ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
A joint model to identify and align bilingual named entities
Computational Linguistics
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Translingual equivalence refers to the relationship between expressions of the same meaning from different languages. Identifying translingual equivalence of named entities (NE) can significantly contribute to multilingual natural language processing, such as crosslingual information retrieval, crosslingual information extraction and statistical machine translation. In this paper we present an integrated approach to extract NE translingual equivalence from a parallel Chinese-English corpus.Starting from a bilingual corpus where NEs are automatically tagged for each language, NE pairs are aligned in order to minimize the overall multi-feature alignment cost. An NE transliteration model is presented and iteratively trained using named entity pairs extracted from a bilingual dictionary. The transliteration cost, combined with the named entity tagging cost and word-based translation cost, constitute the multi-feature alignment cost. These features are derived from several information sources using unsupervised and partly supervised methods. A greedy search algorithm is applied to minimize the alignment cost. Experiments show that the proposed approach extracts NE translingual equivalence with 81% F-score and improves the translation score from 7.68 to 7.74.