  We present a new context based event indexing and event ranking model for
News Articles. The context event clusters formed from the UNL Graphs uses the
modified scoring scheme for segmenting events which is followed by clustering
of events. From the context clusters obtained three models are developed-
Identification of Main and Sub events; Event Indexing and Event Ranking. Based
on the properties considered from the UNL Graphs for the modified scoring main
events and sub events associated with main-events are identified. The temporal
details obtained from the context cluster are stored using hashmap data
structure. The temporal details are place-where the event took; person-who
involved in that event; time-when the event took place. Based on the
information collected from the context clusters three indices are generated-
Time index, Person index, and Place index. This index gives complete details
about every event obtained from context clusters. A new scoring scheme is
introduced for ranking the events. The scoring scheme for event ranking gives
weight-age based on the priority level of the events. The priority level
includes the occurrence of the event in the title of the document, event
frequency, and inverse document frequency of the events.
