Yet another approach to understanding news event evolution

With information explosion on the Internet, only returning ranked documents by search engines cannot satisfy people’s requirements on news events understanding. A more intelligent news events search engine should not only retrieve all related documents about a specific event, but also provide a global view about how the event originates and evolves. In order to solve this challenge, two tasks, event news retrieval and eventline generation should be processed. For event news retrieval, existing approaches mainly focus on the document-level similarity to retrieve related news documents, while external knowledge is not effectively taken into consideration. To this end, we propose a similarity model named Event-Oriented Similarity combining the document-level with the knowledge-level similarity to retrieve news documents related to the specific event. For eventline generation, in order to outline the event structure more accurately, we construct an Event-Oriented Similarity Graph to represent the relationship among retrieved event news documents and develop a community detection algorithm to segment sub-events which are consequently chained into a cohesive eventline. Experimental results on real-world datasets demonstrate that the proposed approach outperforms existing methods.