@inproceedings{HermannHochleitnerKellneretal.2014,
  author    = {Martin Hermann and Michael Hochleitner and Sarah Kellner and Simon Preissner and Desislava Zhekova},
  title     = {Nessy: A Hybrid Approach to Named Entity Recognition for German},
  isbn      = {978-3-934105-47-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:hil2-opus-3071},
  year      = {2014},
  abstract  = {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.},
  language  = {en}
}