JERARTOP: A New Topic Detection System

  • Aurora Pons-Porrata
  • Rafael Berlanga-Llavori
  • José Ruiz-Shulcloper
  • Juan Manuel Pérez-Martínez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


In this paper we present an on-line detection system, named JERARTOP, which goes beyond traditional detection systems, because it generates the implicit knowledge of a stream of documents. This knowledge is expressed as a taxonomy of topics/events, which is automatically built by the system in an incremental way. Moreover, the proposed detection system also annotates each detected topic using a set of predefined subjects, as well as it provides a summary for the topic. The experimental results demonstrate its usefulness and its effectiveness as a detection system.


Document Cluster Cluster Representative Topic Detection Cosine Measure Global Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Aurora Pons-Porrata
    • 1
  • Rafael Berlanga-Llavori
    • 2
  • José Ruiz-Shulcloper
    • 3
  • Juan Manuel Pérez-Martínez
    • 2
  1. 1.Universidad de OrienteSantiago de CubaCuba
  2. 2.Universitat Jaume ICastellónSpain
  3. 3.Advanced Technologies Application CenterMINBASCuba

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