Overlapping Target Event and Story Line Detection of Online Newspaper Articles

Abstract

Event detection from text data is an active area of research. While the emphasis has been on event identification and labeling using a single data source, this work considers event and story line detection when using a large number of data sources. In this setting, it is natural for different events in the same domain, e.g. violence, sports, politics, to occur at the same time and for different story lines about the same event to emerge. To capture events in this setting, we propose an algorithm that detects events and story lines about events for a target domain. Our algorithm leverages a multi-relational sentence level semantic graph and well known graph properties to identify overlapping events and story lines within the events. We evaluate our approach on two large data sets containing millions of news articles from a large number of sources. Our empirical analysis shows that our approach improves the detection precision and recall by 10% to 25%, while providing complete event summaries.