Paper: ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes

ACL ID S14-2045
Title ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes the system used in Task-7 (Analysis of Clinical Text) of SemEval-2014 for detecting disorder men- tions and associating them with their re- lated CUI of UMLS 1 . For Task-A, a CRF based sequencing algorithm was used to find different medical entities and a binary SVM classifier was used to find relation- ship between entities. For Task-B, a dic- tionary look-up algorithm on a customized UMLS-2012 dictionary was used to find relative CUI for a given disorder mention. The system achieved F-score of 0.714 for Task A & accuracy of 0.599 for Task B when trained only on training data set, and it achieved F-score of 0.755 for Task A & accuracy of 0.646 for Task B when trained on both training as well as development data set. Our system was placed 3rd for both task A and B.