  We describe our work on information extraction in medical documents written
in German, especially detecting negations using an architecture based on the
UIMA pipeline. Based on our previous work on software modules to cover medical
concepts like diagnoses, examinations, etc. we employ a version of the NegEx
regular expression algorithm with a large set of triggers as a baseline. We
show how a significantly smaller trigger set is sufficient to achieve similar
results, in order to reduce adaptation times to new text types. We elaborate on
the question whether dependency parsing (based on the Stanford CoreNLP model)
is a good alternative and describe the potentials and shortcomings of both
approaches.
