  Background: There has been growing research interest in automated answering
of questions or generation of summary of free form text such as news article.
In order to implement this task, the computer should be able to identify the
sequence of events, duration of events, time at which event occurred and the
relationship type between event pairs, time pairs or event-time pairs. Specific
Problem: It is important to accurately identify the relationship type between
combinations of event and time before the temporal ordering of events can be
defined. The machine learning approach taken in Mani et. al (2006) provides an
accuracy of only 62.5 on the baseline data from TimeBank. The researchers used
maximum entropy classifier in their methodology. TimeML uses the TLINK
annotation to tag a relationship type between events and time. The time
complexity is quadratic when it comes to tagging documents with TLINK using
human annotation. This research proposes using decision tree and parsing to
improve the relationship type tagging. This research attempts to solve the gaps
in human annotation by automating the task of relationship type tagging in an
attempt to improve the accuracy of event and time relationship in annotated
documents. Scope information: The documents from the domain of news will be
used. The tagging will be performed within the same document and not across
documents. The relationship types will be identified only for a pair of event
and time and not a chain of events. The research focuses on documents tagged
using the TimeML specification which contains tags such as EVENT, TLINK, and
TIMEX. Each tag has attributes such as identifier, relation, POS, time etc.
