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Research


Research Interests

  • Machine Learning (especially graphical models)
  • Active Learning (especially efficient approaches to real-world learning and annotation problems)
  • Named Entity Recognition and Normalization
  • Natural Language Processing of biomedical texts
  • NLP Frameworks (i.e. UIMA, GATE)
  • Annotation "as a science" (modelling annotation costs and the annotation process, cost-conscious methods to training material creation)
  • Application of above methods for large-scale information extraction and data mining

Research Projects Involved

Activities

Awards

  • Best Paper Award at KCAP 2009