D\'etection de l'incertitude et de la n\'egation : un \'etat de l'art (Identifying uncertainty and negation's cues and scope : State of the art One of the goals of our endeavours is to turn a corpus of medical documents into more easily readable structured data)
L{'}un des objectifs de nos travaux, {\a} terme, est de transformer un corpus de documents m{\'e}dicaux en donn{\'e}es structur{\'e}es pour en faciliter l{'}exploitation. Ainsi, il est n{\'e}cessaire non seulement de d{\'e}tecter les concepts m{\'e}dicaux {\'e}voqu{\'e}s, mais aussi d{'}int{\'e}grer un processus capable d{'}identifier le contexte dans lequel est {\'e}voqu{\'e} chaque concept m{\'e}dical. Dans cet article, nous revenons principalement sur les syst{\e}mes par apprentissage supervis{\'e} qui ont {\'e}t{\'e} propos{\'e} pour la d{\'e}tection de l{'}incertitude et de la n{\'e}gation. Ces dix derni{\`e}res ann{\'e}es, les travaux pour d{\'e}tecter l{'}incertitude et la n{\'e}gation dans les textes en anglais ont donn{\'e} des r{\'e}sultats satisfaisants. Cependant, il existe encore une marge de progression non-n{\'e}gligeable.
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