anchor.diff:  };
   ----
   
  -#### Caveat
  +### Caveat
   
   Mind, that it is currently not tested to import data across batch task boundaries. That is, in the example above, the `featureExtractionTask` does not directly import data from the `preprocessingTask`. Instead, the `crossvalidationTask` imports the data from the `preprocessingTask` and forwards it to the `featureExtractionTask` via the file names in the fold dimension.
   
  -#### Summary
  +### Summary
   
   If a cross-validation task depends on the output of a preprocessing task, it is impossible to set up a static parameter dimension for the _trainingSet_ and _testSet_, because it depends on the data created by the preprocessing task. The tutorial has illustrated how to create a nested batch task which dynamically creates its own parameter space using a `FoldDimensionBundle` based on the output of the preprocessing task.
positive.code:     
  ## Cross validation
  
  ### Cross validation tutorial
  
  This is a brief introduction to running an experiment that has a cross validation part. The most important things to learn in this example is, how to set a parameter space dimension which is created dynamically via a nested `BatchTask` and how to use a `FoldDimensionBundle`.
  
  #### Basic tasks
  
  Assume you have these tasks:
  
  
negative.code:    * *line 20-22*: add the sub-tasks that perform the cross-validation.
     * *line 24-27*: configure the overall batchTask that runs the preprocessing and the cross-validation.
  
  ### Reader 
  
  The following snipped illustrates how to use the _trainingSet_ parameter `files_training_` and `filesRoot` to configure the DKPro Core ASL `XmiReader` component.
  
  