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Extracting Key Entities and Significant Events from Online Daily News

  • Mingrong Liu
  • Yicen Liu
  • Liang Xiang
  • Xing Chen
  • Qing Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

To help people obtain the most important information daily in the shortest time, a novel framework is presented for simultaneous key entities extraction and significant events mining from daily web news. The technique is mainly based on modeling entities and news documents as weighted undirected bipartite graph, which consists of three steps. First, key entities are extracted by scoring all candidate entities on a specific day and tracking their trends within a specific time window. Second, a weighted undirected bipartite graph is built based on entities and related news documents, then mutual reinforcement is imposed on the bipartite graph to rank both of them. Third, clustering on news articles generates daily significant events. Experimental study shows effectiveness of this approach.

Keywords

web news mining entity mutual reinforcement event 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mingrong Liu
    • 1
  • Yicen Liu
    • 1
  • Liang Xiang
    • 1
  • Xing Chen
    • 1
  • Qing Yang
    • 1
  1. 1.National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijingChina

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