Context Specific Event Model For News Articles

TL;DR

This paper introduces a novel context-based event indexing and ranking model for news articles, utilizing UNL Graphs to identify, cluster, and prioritize events with detailed temporal, location, and involved person information.

Contribution

It proposes a new event clustering, indexing, and ranking framework based on UNL Graphs and a modified scoring scheme for news articles.

Findings

Effective event clustering from UNL Graphs

Comprehensive event indexing with temporal, person, and place details

A novel scoring scheme for event ranking based on priority factors

Abstract

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.