We developed three ensemble learning approaches for recognizing disorder entities and a Vector Space Model based method for encoding . Our approaches achieved top rank in both subtasks , with the best F measure of 0.813 for entity recognition and the best accuracy of 74.1 % for encoding , indicating the proposed approaches are promising . 1 Introduction In recent years , clinical natural language processing ( NLP ) has received great attention for its critical role in unlocking information embedded in clinical documents . Leveraging such information can facilitate the secondary1 use of electronic health record ( EHR ) data to This work is licensed under a Creative Commons Attribution 4.0 International Licence .