• Media type: E-Article
  • Title: Nessy: A Hybrid Approach to Named Entity Recognition for German
  • Contributor: Hermann, Martin [Author]; Hochleitner, Michael [Author]; Kellner, Sarah [Author]; Preissner, Simon [Other]; Zhekova, Desislava [Other]
  • imprint: Hildesheim: Universitätsbibliothek Hildesheim, 2014
  • Published in: Faaß, Gertrud: WORKSHOP PROCEEDINGS OF THE 12TH EDITION OF THE KONVENS CONFERENCE ; (2014), Seite 139-143
  • Language: English
  • ISBN: 9783934105478
  • Identifier:
  • Keywords: Online-Ressource
  • Origination:
  • Footnote:
  • Description: In this paper we present Nessy (Named Entity Searching System) and its application to German in the context of the GermEval 2014 Named Entity Recognition Shared Task (Benikova et al., 2014a). We tackle the challenge by using a combination of machine learning (Naive Bayes classification) and rule-based methods. Altogether, Nessy achieves an F-score of 58.78% on the final test set.
  • Access State: Open Access