We demonstrate the need to rethink resources, features and methods of learning temporal relations between events in different domains with the help of experiments in learning temporal relations in clinical text.
we observe that we get better results in learning to rank chains of medical events to derive temporal relations (and their inverses) than learning a classifier for the same task.