class_docstr: This builds a bag of words for both the text and the hypothesis after
  throwing away some stopwords, then calculates overlap and difference.
docstr: Compute the extraneous material in the hypothesis.
  
  :param toktype: distinguish Named Entities from ordinary words
  :type toktype: 'ne' or 'word'
code: def hyp_extra(self, toktype, debug=True):
      """
      Compute the extraneous material in the hypothesis.
  
      :param toktype: distinguish Named Entities from ordinary words
      :type toktype: 'ne' or 'word'
      """
      ne_extra = {token for token in self._hyp_extra if self._ne(token)}
      if toktype == "ne":
          return ne_extra
      elif toktype == "word":
          return self._hyp_extra - ne_extra
      else:
          raise ValueError("Type not recognized: '%s'" % toktype)
  
file_path: nltk/nltk/classify/rte_classify.py
imports: from nltk.classify.maxent import MaxentClassifier
  from nltk.classify.util import accuracy
  from nltk.tokenize import RegexpTokenizer
label_desc: Use image node_id 3 for calling the RTEFeatureExtractor obj's underlying member method code with example usage: obj.hyp_extra(toktype, debug) and returns: ne_extra, unknown