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This paper describes our participation in task 7 of SemEval 2014, which focuses on analysis of clinical text. The task is divided into two parts: recognizing mentions of concepts that belong to the UMLS (Unified Medical Language System) semantic group disorders, and mapping each disorder to a unique UMLS CUI (Concept Unique Identifier), if possible. For identifying and mapping disorders belonging to the UMLS meta thesaurus, we explore two tools: Metamap and T-mapper. Additionally, a Named Entity Recognition system, based on a maximum entropy model, was implemented to identify other disorders.

MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7