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Abstract:
Time is an important dimension as it aids in disambiguating and understanding news-
worthy events that happened in the past. It helps in chronological ordering of events to
understand its causality, evolution, and ramifications. In Information Retrieval, time
alongside text is known to improve the quality of search results. So, making use of
the temporal dimensionality in the text-based analysis is an interesting idea to explore.
Considering the importance of time, methods to automatically resolve temporal foci’s
of events are essential. In this thesis, we try to solve this research question by training
our models on two different kinds of corpora and then evaluate on a set of historical
event-queries.