  Temporal Information and Event Markup Language (TIE-ML) is a markup strategy
and annotation schema to improve the productivity and accuracy of temporal and
event related annotation of corpora to facilitate machine learning based model
training. For the annotation of events, temporal sequencing, and durations, it
is significantly simpler by providing an extremely reduced tag set for just
temporal relations and event enumeration. In comparison to other standards, as
for example the Time Markup Language (TimeML), it is much easier to use by
dropping sophisticated formalisms, theoretical concepts, and annotation
approaches. Annotations of corpora using TimeML can be mapped to TIE-ML with a
loss, and TIE-ML annotations can be fully mapped to TimeML with certain
under-specification.
