ThinkMiners: Disorder Recognition using Conditional Random Fields and Distributional Semantics

In 2014, SemEval organized multiple challenges on natural language processing and information retrieval. One of the task was analysis of the clinical text. This challenge is further divided into two tasks. The task A of the challenge was to extract disorder mention spans in the clinical text and the task B was to map each of the disorder mentions to a unique Unified Medical Language System Concept Unique Identifier. We participated in the task A and developed a clinical disorder recognition system. The proposed system consists of a Conditional Random Fields based approach to recognize disorder entities. The SemEval challenge organizers manually annotated disorder entities in 298 clinical notes, of which 199 notes were used for training and 99 for development. On the test data, our system achieved the Fmeasure of 0.844 for entity recognition in relaxed and 0.689 in strict evaluation.