Visualization of news articles

Abstract

This paper presents a system for visualization of large amounts of new stories. In the first phase, the new stories are preprocessed for the purpose of name-entity extraction. Next, a graph of relationships between the extracted name entities is created, where each name entity represents one vertex in the graph and two name entities are connected if they appear in the same document. The graph of entities is presented as a local neighborhood enriched with additional contextual information in the form of characteristic keywords and related name entities connected to the entity in the focus. Operations for browsing a graph are implemented to be efficient enabling quick capturing of large amounts of information present in the original text.