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6 septembre 2016
- handle: 10670/1.h9hriw
- hal: hal-01365928
info:eu-repo/semantics/OpenAccess
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Medical documents Named Entity RecognitionSujets proches
Documents Documents, Legal Documents Public documents Official publications Government documents Documents Documents Indentures Documents Charters--Law and legislation Documents Manuscript repositories Manuscript depositories Manuscripts--Repositories Manuscripts--DepositoriesCiter ce document
Lydia-Mai Ho-Dac et al., « LITL at CLEF eHealth2016: recognizing entities in French biomedical documents », HAL-SHS : linguistique, ID : 10670/1.h9hriw
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Résumé
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.