In this paper, we demonstrate our approach for identification of events, time expressions and temporal relations among them. This work was carried out as part of SemEval-2016 Challenge Task 12: Clinical TempEval. The task comprises six sub-tasks: identification of event spans, time spans and their attributes, document time relation and the narrative container relations among events and time expressions. We have participated in all six subtasks. We have provided with a manually annotated dataset which comprises of training dataset (293 documents), development dataset (147 documents) and 151 documents as test dataset. We have submitted our work as two systems for the challenge. One system is developed using machine learning techniques, Conditional Random Fields (CRF) and Support Vector machines (SVM) and the other system is developed using deep neural network (DNN) techniques. The results show that both systems have given relatively same performance on these tasks.

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