Automatic processing of multilingual medical terminology: applications to thesaurus enrichment and cross-language information retrieval

  • Authors:
  • H. Déjean;E. Gaussier;J. -M. Renders;F. Sadat

  • Affiliations:
  • Xerox Research Centre Europe, 6 Chemin de Maupertuis, F-38240 Meylan, France;Xerox Research Centre Europe, 6 Chemin de Maupertuis, F-38240 Meylan, France;Xerox Research Centre Europe, 6 Chemin de Maupertuis, F-38240 Meylan, France;Nara Institute of Science and Technology, Ikoma, Nara, Japan

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Objectives:: We present in this article experiments on multi-language information extraction and access in the medical domain. For such applications, multilingual terminology plays a crucial role when working on specialized languages and specific domains. Material and methods:: We propose firstly a method for enriching multilingual thesauri which extracts new terms from parallel corpora, and secondly, a new approach for bilingual lexicon extraction from comparable corpora, which uses a bilingual thesaurus as a pivot. We illustrate their use in multi-language information retrieval (English/German) in the medical domains. Results:: Our experiments show that these automatically extracted bilingual lexicons are accurate enough (85% precision for term extraction) for semi-automatically enriching mono- or bi-lingual thesauri such as the universal medical language system, and that their use in cross-language information retrieval significantly improves the retrieval performance (from 22 to 40% average precision) and clearly outperforms existing bilingual lexicon resources (both general lexicons and specialized ones). Conclusion:: We show in this paper first that bilingual lexicon extraction from parallel corpora in the medical domain could lead to accurate, specialized lexicons, which can be used to help enrich existing thesauri and second that bilingual lexicons extracted from comparable corpora outperform general bilingual resources for cross-language information retrieval.