Building specialized bilingual lexicons using large-scale background knowledge

D. Bouamor 1 A. Popescu 1 N. Semmar 1 P. Zweigenbaum 2
1 DIASI - Département Intelligence Ambiante et Systèmes Interactifs
2 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur
Abstract : Bilingual lexicons are central components of machine translation and cross-lingual information retrieval systems. Their manual construction requires strong expertise in both languages involved and is a costly process. Several automatic methods were proposed as an alternative but they often rely on resources available in a limited number of languages and their performances are still far behind the quality of manual translations. We introduce a novel approach to the creation of specific domain bilingual lexicon that relies on Wikipedia. This massively multilingual encyclopedia makes it possible to create lexicons for a large number of language pairs. Wikipedia is used to extract domains in each language, to link domains between languages and to create generic translation dictionaries. The approach is tested on four specialized domains and is compared to three state of the art approaches using two language pairs: French-English and Romanian-English. The newly introduced method compares favorably to existing methods in all configurations tested.
Keywords : Machine components Natural language processing systems Search engines Automatic method Back-ground knowledge Bilingual lexicons Central component Cross-lingual information retrieval Language pairs Machine translations State-of-the-art approach Translation (languages) Information retrieval systems Computational linguistics
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Conference papers
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Contributor : Léna Le Roy <>
Submitted on : Thursday, July 19, 2018 - 3:57:28 PM
Last modification on : Saturday, May 4, 2019 - 1:21:27 AM

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D. Bouamor, A. Popescu, N. Semmar, P. Zweigenbaum. Building specialized bilingual lexicons using large-scale background knowledge. 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, Oct 2013, Seattle, United States. pp.479-489. ⟨cea-01844695⟩

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