anchor.diff:  the file. Beware that Gensim's logging produces a lot of detailed
   output.
   
  +[source, python]
   ----
   import logging
   logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
  
positive.code: higher), Eclipse (4.3.x), the Maven Integration for Eclipse (M2E) plugin
  and the DKPro Core ASL 1.8.0 or higher have to be installed on your
  computer. For further information
  see link:http://dkpro.github.io/dkpro-core/pages/java-intro.html[First
  Programming Steps with DKPro Core].
  
  Some of the analysis components can be run with different models. For
  processing you can choose the component and the model that suits your
  interests the most
  from link:http://dkpro.github.io/dkpro-core/releases/1.7.0/components.html[this
  list]. For example, if you want to classify entities such as the names
  of persons, locations, expressions of times, organisations and so on
  there are two selectable components. StanfordNamedEntityRecognizer and
  OpenNlpNameFinder both are suitable for Named Entity Recognition. But if
  you are working with a German text StanfordNamedEntityRecognizer would
  be the better choice as you see in
  the link:http://dkpro.github.io/dkpro-core/releases/1.7.0/models.html[list
  of models].
  
  [[UsingTreeTagger]]
  
negative.code: 
  When the Java environment is prepared, you
  can link:https://github.com/DARIAH-DE/DARIAH-DKPro-Wrapper[download the latest binary]. Select
  the file named dariah-dkpro-wrapper-{version}.zip and unpack it somewhere
  easily accessible. As a next step we need to navigate to this folder
  using the command line.
  
  