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1 CLLE-ERSS - Cognition, Langues, Langage, Ergonomie 2 Université de Toulouse - Jean Jaurès

Abstract : This paper describes the participation of master-s students LITL programme, university of Toulouse and their teachers to the CLEF eHealth 2016 campaign. Two runs were submitted for task 2 multilingual information extraction which consisted in the recognition and categorization of medical entities in French biomedical documents. The system used consists of a CRF classier based on a number of dierent features POS tagging, generic word lists and syntactic parsing. In addition , several patterns were used on the CRF-s output in order to extract more complex entities. The best run achieved high precision 0.640.78 but lower recall 0.320.40, with an overall F1-measure of 0.430.53.

Keywords : Named Entity Recognition Medical documents

Autor: Lydia-Mai Ho-Dac - Ludovic Tanguy - Céline Grauby - Aurore Heu Mby - Justine Malosse - Laura Rivière - Amélie Veltz-Mauclair -



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