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There are tons of news articles generated every day reflecting the activitiesof key roles such as people, organizations and political parties. Analyzingthese key roles allows us to understand the trends in news. In this paper, wepresent a demonstration system that visualizes the trend of key roles in newsarticles based on natural language processing techniques. Specifically, weapply a semantic role labeler and the dynamic word embedding technique tounderstand relationships between key roles in the news across different timeperiods and visualize the trends of key role and news topics change over time.

Visualizing Trends of Key Roles in News Articles