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This paper presents the results of the IxaMed team at the SemEval-2014 Shared Task 7 on Analyzing Clinical Texts. We have developed three different systems based on: a) exact match, b) a general-purpose morphosyntactic analyzer enriched with the SNOMED CT terminology content, and c) a perceptron sequential tagger based on a Global Linear Model. The three individual systems result in similar f-score while they vary in their precision and recall. We have also tried direct combinations of the individual systems, obtaining considerable improvements in performance.

IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical Texts