We implemented an end-to-end system for disorder identification and slot filling. For identifying spans for both disorders and their attributes, we used a linear chain conditional random field (CRF) approach coupled with cTAKES for pre-processing. For combining disjoint disorder spans, finding relations between attributes and disorders, and attribute normalization, we used 12-regularized 12-loss linear support vector machine (SVM) classification. Disorder CUIs were identified using a back-off approach to YTEX lookup (CUAB1) or NLM UTS API (CUAB2) if the target text was not found in the training data. Our best system utilized UMLS semantic type features for disorder/attribute span identification and the NLM UTS API for normalization. It was ranked 12th in Task 1 (disorder identification) and 6th in Task 2b (disorder identification and slot filling) with a weighted F Measure of 0.711.
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