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

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.

Hybrid methods using genetic algorithms for global optimization.

This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and "hill-climbing" methods in order to find a better compromise to the trade-off. Inspired by biology and especially by the manner in which living beings adapt themselves to their environment, these hybrid methods involve two interwoven levels of optimization, namely evolution (genetic algorithms) and individual learning (Quasi-Newton), which cooperate in a global process of optimization. One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter.

Phosphoinositides in barley aleurone layers and gibberellic Acid-induced changes in metabolism.

Phospholipids of barley (Hordeum vulgare L. cv Himalaya) aleurone layers were labeled with myo-[2-(3)H]inositol or [(32)Pi], extracted, and analyzed by physical (chromatography) and chemical (deacylation) techniques. Three phospholipids were found to incorporate both myo-[2-(3)H]inositol and [(32)Pi]-phosphatidylinositol, phosphatidylinositol-monophosphate, and phosphatidylinositol-bisphosphate. Stimulation of [(3)H]inositol prelabeled aleurone layers with GA(3) showed enhanced incorporation of label into phosphatidylinositol within 30 seconds and subsequent rapid breakdown. Stimulation of phosphatidylinositol labeling observed in these studies is the earliest response of aleurone cells to gibberellic acid reported.