Discovering Event Evolution Graphs Based on News Articles Relationships

There are many news articles reported online everyday. Within an ongoing topic, people can find a huge amount of news articles. A topic often consists of several events, and people are interested in the whole evolution of a topic along a timeline. This requests for finding and identifying the dependent relationships between events. In order to understand the whole evolution of a topic effectively, we propose a framework of event relationship analysis. We define three kinds of event relationships which are coccurrence dependence relationship, event reference relationship, and temporal proximity relationship for modeling how an event is dependent on another event within a topic. Through combining three kinds of relationships, we can discover an Event Evolution Graph (EEG) for users to view the evolution of a topic. Experiments conducted on a real data set show that our method outperforms baseline methods.