Discovering event evolution graphs from newswires

ABSTRACT

In this paper, we propose an approach to automatically mine event evolution graphs from newswires on the Web. Event evolution graph is a directed graph in which the vertices and edges denote news events and the evolutions between events respectively, in a news affair. Our model utilizes the content similarity between events and incorporates temporal proximity and document distributional proximity as decaying functions. Our approach is effective in presenting the inside developments of news affairs along the timeline, which can facilitate users' information browsing tasks.

REFERENCES

• 1: Juha Makkonen, Investigations on event evolution in TDT, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop, p.43-48, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073416.1073424]
• 2: Ramesh Nallapati , Ao Feng , Fuchun Peng , James Allan, Event threading within news topics, Proceedings of the thirteenth ACM international conference on Information and knowledge management, November 08-13, 2004, Washington, D.C., USA [doi>10.1145/1031171.1031258]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models; Clustering

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
event evolution, event evolution graph, knowledge management, web content mining