Event threading within news topics

  • Authors:

  • Ramesh Nallapati;Ao Feng;Fuchun Peng;James Allan

  • Affiliations:

  • University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA

  • Venue:
  • Proceedings of the thirteenth ACM international conference on Information and knowledge management
  • Year:
  • 2004

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Abstract

With the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner. Previous research focused only on organizing news stories by their topics into a flat hierarchy. We believe viewing a news topic as a flat collection of stories is too restrictive and inefficient for a user to understand the topic quickly. In this work, we attempt to capture the rich structure of events and their dependencies in a news topic through our event models. We call the process of recognizing events and their dependencies event threading. We believe our perspective of modeling the structure of a topic is more effective in capturing its semantics than a flat list of on-topic stories. We formally define the novel problem, suggest evaluation metrics and present a few techniques for solving the problem. Besides the standard word based features, our approaches take into account novel features such as temporal locality of stories for event recognition and time-ordering for capturing dependencies. Our experiments on a manually labeled data sets show that our models effectively identify the events and capture dependencies among them.