Supervised Named Entity Recognition for Clinical Data

Clinical Named Entity Recognition is a part of Task 1b, organised by CLEF eHealth organisation in 2015. The aim is to automatically identify clinically relevant entities in medical text in French. A supervised learning approach has been used for training the tagger. For the purpose of training, Conditional Random Fields(CRF) has been used. An extensive set of features was used for training. Precision, recall and F1 Score were used as evaluation metrics. Ten fold cross validation technique was used to evaluate the system. The best precision obtained was 0.91 and the best recall obtained was 0.66. After the test results were announced, the best F1 score obtained for exact matching was 0.67 and for relaxed case (i.e. inexact matching), it was 0.73.