UWM: Disorder Mention Extraction from Clinical Text Using CRFs and Normalization Using Learned Edit Distance Patterns
Omid Amir Ghiasvand, Rohit J. Kate · 2014
This paper describes Team UWM’s system for the Task 7 of SemEval 2014 that does disorder mention extraction and normalization from clinical text. For the disorder mention extraction (Task A), the system was trained using Conditional Random Fields with features based on words, their POS tags and semantic types, as well as features based on MetaMap matches. For the disorder mention normalization (Task B), variations of disorder mentions were considered whenever exact matches were not found in the training data or in the UMLS. Suitable types of variations for disorder mentions were automatically learned using a new method based on edit distance patterns. Among nineteen participating teams, UWM ranked third in Task A with 0.755 strict F-measure and second in Task B with 0.66 strict accuracy.