This paper describes a system developed for the disorder identification subtask within task 14 of SemEval 2015. The developed system is based on a chain of two modules, one for recognition and another for normalization. The recognition module is based on an adapted version of the Stanford NER system to train CRF models in order to recognize disorder mentions. CRF models were build based on a novel encoding of entity spans as token classifications to also consider non-continuous entities, along with a rich set of features based on (i) domain lexicons and (ii) Brown clusters inferred from a large collection of clinical texts. For disorder normalization, we (i) generated a non ambiguous dictionary of abbreviations from the labelled files, using it together with (ii) an heuristic method based on similarity search and (iii) a comparison method based on the information content of each disorder. The system achieved an F-measure of 0.740 (the second best), with a precision of 0.779, a recall of 0.705.

This paper describes our participation on Task 7 of SemEval 2014, which focused on the recognition and disambiguation of medical concepts. We used an adapted version of the Stanford NER system to train CRF models to recognize textual spans denoting diseases and disorders, within clinical notes. We considered an encoding that accounts with noncontinuous entities, together with a rich set of features (i) based on domain specific lexicons like SNOMED CT, or (ii) leveraging Brown clusters inferred from a large collection of clinical texts. Together with this recognition mechanism, we used a heuristic similarity search method, to assign an unambiguous identifier to each concept recognized in the text.Our best run on Task A (i.e., in the recognition of medical concepts in the text) achieved an F-measure of 0.705 in the strict evaluation mode, and a promising F-measure of 0.862 in the relaxed mode, with a precision of 0.914. For Task B (i.e., the disambiguation of the recognized concepts), we achieved less promising results, with an accuracy of 0.405 in the strict mode, and of 0.615 in the relaxed mode.