A Supervised Abbreviation Resolution System for Medical Text

We present our participation in Task 2 of the 2013 CLEFeHEALTH Challenge, whose goal was to determine the UMLS concept unique identifier (CUI), if available, of an abbreviation or acronym. We hypothesize that considering only the abbreviations of the training corpus could be sufficient to provide a strong baseline for this task. We therefore test how a fully supervised approach, which predicts the CUI of an abbreviation based only on the abbreviations and CUIs seen in the training corpus, can fare on this task. We adapt to this task the processing pipeline we developed for CLEF-eHEALTH Task 1, entity detection: a supervised MaxEnt model based on a set of features including UMLS Concept Unique Identifiers, complemented here with a rule-based component for document headers. This system confirmed our hypothesis, and was evaluated at 0.664 accuracy (strict) and 0.672 accuracy (relaxed), ranking second out of five teams.