anchor.diff:  different languages and different types of text. An independently
   developed NER component trained on German 19th century novels will be
   included in a later version of the pipeline to address use cases like
  -this. In the meanwhile, and as a more generic approach, you can also try
  -filtering all proper nouns (NP).
  +this. The following lines will add all named entities to the stopword list.
  +
  +[source, python]
  +----
  +df = df.groupby('NamedEntity')
  +
  +names = df.get_group('B-PER')['Lemma'].values.astype(str)
  +
  +names += df.get_group('I-PER')['Lemma'].values.astype(str)
  +
  +stopwords += names.tolist()
  +----
  +
  +In the meanwhile, and as a more generic approach, we filter
  +out all proper nouns (NP).
   
   [source, python]
   ----
  
positive.code: 
  * Linux (Ubuntu 14.04, 15.10)
  
  * Windows 7/8
  
  Furthermore, the pipeline depends on a *internet connection* when
  running to download the models for the current configuration. It does
  
negative.code:                 docs.append(doc['Lemma'].values.astype(str))    # add the rest
                  doc_labels.append(file.split(".")[0]+" #"+str(i))
   
      print("\nnormalizing and vectorizing ...\n")        # cf. https://radimrehurek.com/gensim/tut1.html
   
      texts = [[word for word in doc if word not in stopwords] for doc in docs]       # remove stopwords
  