Enhancing clinical concept extraction with distributional semantics

► Explored the use of distributional semantics for a sequence classification task such as concept extraction. ► Evaluated different distributional semantic models based on their ability to predict the semantic types of terms. ► Proposed semi supervised learning in a framework that is agnostic to the underlying supervised learning algorithm. ► Improved the F-score of a state of the art clinical concept extraction by 2%.