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

A Case Study on Pros and Cons of Regular Expression Detection and  Dependency Parsing for Negation Extraction from German Medical Documents.  Technical Report