Abstract
In the course of our current research on automatic information extraction from medical electronic literature, we have been facing the need to map big corpora onto the concepts of the UMLS Metathesaurus, both in French and in English. In order to meet our specific needs in terms of processing speed, we have developed a lightweight UMLS tagger, MetaCoDe, that processes large text collections at an acceptable speed, but at the cost of the sophistication of the treatments. In this paper, we describe MetaCoDe and evaluate its quality, allowing potential users to balance the gain in speed against the loss in quality.
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Delbecque, T., Zweigenbaum, P. (2007). MetaCoDe: A Lightweight UMLS Mapping Tool. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_32
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DOI: https://doi.org/10.1007/978-3-540-73599-1_32
Publisher Name: Springer, Berlin, Heidelberg
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