Integrated cTAKES for concept mention detection and normalization

We participated Task 1 using an existing system MedTagger implemented in inte-grated cTAKES (icTAKES). The concept mention detection is based on Conditional Random Fields (CRF) and the concept mention normalization is based on a greedy dictionary lookup algorithm. A distinctive feature in MedTagger compared to other concept mention detection systems is the incorporation of dictionary lookup results into a machine learning framework for sequential labeling. Dictionary lookup results of MedLex and semantic vectors representing distributed semantics were used as features. Overall, the precision, recall, and F-measure of our best run for concept mention are 0.8, 0.573, and 0.668 respectively for strict evaluation and 0.939, 0.766, and 0.844 for relaxed evaluation. The accuracy of our best run for concept men-tion normalization is 54.6% and 87.0% for strict and relaxed mapping, respectively.