01592nas a2200229   4500000000100000000000100001008004100002260001200043653002700055653000800082653002100090100001800111700001900129700001900148700001800167245006800185856009800253300001000351490000600361520098100367022001401348       2015                            d  c06/201510aInformation Technology10aNLP10aMedical Entities1 aAicha Ghoulam1 aFatiha Barigou1 aGhalem Belalem1 aFarid Meziane00aUsing Local Grammar for Entity Extraction from Clinical Reports  uhttp://www.ijimai.org/JOURNAL/sites/default/files/files/2015/05/ijimai20153_3_2_pdf_97545.pdf  a16-240 v33 aInformation Extraction (IE) is a natural language processing (NLP) task whose aim is to analyze texts written in natural language to extract structured and useful information such as named entities and semantic relations linking these entities. Information extraction is an important task for many applications such as bio-medical literature mining, customer care, community websites, and personal information management. The increasing information available in patient clinical reports is difficult to access. As it is often in an unstructured text form, doctors need tools to enable them access to this information and the ability to search it. Hence, a system for extracting this information in a structured form can benefits healthcare professionals. The work presented in this paper uses a local grammar approach to extract medical named entities from French patient clinical reports. Experimental results show that the proposed approach achieved an F-Measure of 90. 06%.  a1989-1660