class_docstr: An NLTK interface to the VerbNet verb lexicon.
  
  From the VerbNet site: "VerbNet (VN) (Kipper-Schuler 2006) is the largest
  on-line verb lexicon currently available for English. It is a hierarchical
  domain-independent, broad-coverage verb lexicon with mappings to other
  lexical resources such as WordNet (Miller, 1990; Fellbaum, 1998), XTAG
  (XTAG Research Group, 2001), and FrameNet (Baker et al., 1998)."
  
  For details about VerbNet see:
  https://verbs.colorado.edu/~mpalmer/projects/verbnet.html
class_docstr_tok: ["An","NLTK","interface","to","the","VerbNet","verb","lexicon",".","From","the","VerbNet","site",":","``","VerbNet","(","VN",")","(","Kipper-Schuler","2006",")","is","the","largest","on-line","verb","lexicon","currently","available","for","English",".","It","is","a","hierarchical","domain-independent",",","broad-coverage","verb","lexicon","with","mappings","to","other","lexical","resources","such","as","WordNet","(","Miller",",","1990",";","Fellbaum",",","1998",")",",","XTAG","(","XTAG","Research","Group",",","2001",")",",","and","FrameNet","(","Baker","et","al.",",","1998",")",".","''","For","details","about","VerbNet","see",":","https",":","\/\/verbs.colorado.edu\/~mpalmer\/projects\/verbnet.html"]
docstr: Initialize the indexes ``_lemma_to_class``,
  ``_wordnet_to_class``, and ``_class_to_fileid`` by scanning
  through the corpus fileids.  This doesn't do proper xml parsing,
  but is good enough to find everything in the standard VerbNet
  corpus -- and it runs about 30 times faster than xml parsing
  (with the python ElementTree; only 2-3 times faster
  if ElementTree uses the C implementation).
docstr_tok: ["Initialize","the","indexes","``","_lemma_to_class","``",",","``","_wordnet_to_class","``",",","and","``","_class_to_fileid","``","by","scanning","through","the","corpus","fileids",".","This","does","n't","do","proper","xml","parsing",",","but","is","good","enough","to","find","everything","in","the","standard","VerbNet","corpus","--","and","it","runs","about","30","times","faster","than","xml","parsing","(","with","the","python","ElementTree",";","only","2-3","times","faster","if","ElementTree","uses","the","C","implementation",")","."]
code: def _quick_index(self):
      """
      Initialize the indexes ``_lemma_to_class``,
      ``_wordnet_to_class``, and ``_class_to_fileid`` by scanning
      through the corpus fileids.  This doesn't do proper xml parsing,
      but is good enough to find everything in the standard VerbNet
      corpus -- and it runs about 30 times faster than xml parsing
      (with the python ElementTree; only 2-3 times faster
      if ElementTree uses the C implementation).
      """
      # nb: if we got rid of wordnet_to_class, this would run 2-3
      # times faster.
      for fileid in self._fileids:
          vnclass = fileid[:-4]  # strip the '.xml'
          self._class_to_fileid[vnclass] = fileid
          self._shortid_to_longid[self.shortid(vnclass)] = vnclass
          with self.open(fileid) as fp:
              for m in self._INDEX_RE.finditer(fp.read()):
                  groups = m.groups()
                  if groups[0] is not None:
                      self._lemma_to_class[groups[0]].append(vnclass)
                      for wn in groups[1].split():
                          self._wordnet_to_class[wn].append(vnclass)
                  elif groups[2] is not None:
                      self._class_to_fileid[groups[2]] = fileid
                      vnclass = groups[2]  # for <MEMBER> elts.
                      self._shortid_to_longid[
                          self.shortid(vnclass)
                      ] = vnclass
                  else:
                      assert False, "unexpected match condition"
  
imports: import re
  import textwrap
  from collections import defaultdict
  from nltk.corpus.reader.xmldocs import XMLCorpusReader
label_desc: Use image node_id 12 for calling the VerbnetCorpusReader obj's underlying member method code with example usage: obj._quick_index() without return types