Clustering and mapping related news about violence events on their time-lines

Keeping track of news stories and events as they progress can be a tedious job, but as every day routine most of the web users read and follow many stories and events in news. If an analyst in her area has to follow and map all these according to the time-line they happen, the task quickly becomes overwhelming. We present an online tool which attempts to ease the analyst's task of finding all news articles about an event, and sorting and mapping them on a time-line. We implemented an incremental clustering algorithm working on real-time incoming news, experimenting with different feature sets, including named entities and sentence overlap methods. We evaluated these approaches using Document Understand Conference (DUC) datasets.