TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7

We present our participation in Task 7 of SemEval shared task 2014. The goal of this particular task includes the identification of disorder named entities and the mapping of each disorder to a unique Unified Medical Language System concept identifier, which were referred to as Task A and Task B respectively. We participated in both of these subtasks and used YTEX as a baseline system. We further developed a supervised linear chain Conditional Random Field model based on sets of features to predict disorder mentions. To take benefit of results from both systems we merged these results. Under strict condition our best run evaluated at 0.549 F-measure for Task A and an accuracy of 0.489 for Task B on test dataset. Based on our error analysis we conclude that recall of our system can be significantly increased by adding more features to the Conditional Random Field model and by using another type of tag representation or frame matching algorithm to deal with the disjoint entity mentions. © 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings. All rights reserved.