Enhanced Services for Targeted Information Retrieval by Event Extraction and Data Mining

  • Felix Jungermann
  • Katharina Morik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5039)


We present a framework combining information retrieval with machine learning and (pre-)processing for named entity recognition in order to extract events from a large document collection. The extracted events become input to a data mining component which delivers the final output to specific user’s questions. Our case study is the public collection of minutes of plenary sessions of the German parliament and of petitions to the German parliament.


Web-based Information Services Knowledge Extraction Application Framework 


  1. 1.
    Jungermann, F., Morik, K.: Enhanced services for targeted information retrieval by event extraction and data mining. Technical Report 04/2008, Sonderforschungsbereich 475, University of Dortmund (2008)Google Scholar
  2. 2.
    Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Eliassi-Rad, T., Ungar, L.H., Craven, M., Gunopulos, D. (eds.) Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 935–940. ACM Press, New York (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Felix Jungermann
    • 1
  • Katharina Morik
    • 1
  1. 1.Artificial Intelligence UnitTU Dortmund 

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