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Translation of Biomedical Terms by Inferring Rewriting Rules

Translation of Biomedical Terms by Inferring Rewriting Rules

Vincent Claveau
Copyright: © 2009 |Pages: 18
ISBN13: 9781605662749|ISBN10: 1605662747|ISBN13 Softcover: 9781616925284|EISBN13: 9781605662756
DOI: 10.4018/978-1-60566-274-9.ch006
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MLA

Claveau, Vincent. "Translation of Biomedical Terms by Inferring Rewriting Rules." Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, IGI Global, 2009, pp. 106-123. https://doi.org/10.4018/978-1-60566-274-9.ch006

APA

Claveau, V. (2009). Translation of Biomedical Terms by Inferring Rewriting Rules. In V. Prince & M. Roche (Eds.), Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration (pp. 106-123). IGI Global. https://doi.org/10.4018/978-1-60566-274-9.ch006

Chicago

Claveau, Vincent. "Translation of Biomedical Terms by Inferring Rewriting Rules." In Information Retrieval in Biomedicine: Natural Language Processing for Knowledge Integration, edited by Violaine Prince and Mathieu Roche, 106-123. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-274-9.ch006

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Abstract

This chapter presents a simple yet efficient approach to translate automatically unknown biomedical terms from one language into another. This approach relies on a machine learning process able to infer rewriting rules from examples, that is, from a list of paired terms in two studied languages. Any new term is then simply translated by applying the rewriting rules to it. When different translations are produced by conflicting rewriting rules, we use language modeling to single out the best candidate. The experiments reported here show that this technique yields very good results for different language pairs (including Czech, English, French, Italian, Portuguese, Spanish and even Russian). The author also shows how this translation technique could be used in a cross-language information retrieval task and thus complete the dictionary-based existing approaches.

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