class_docstr: A noun phrase extractor that uses chunk parsing trained with the
  ConLL-2000 training corpus.
class_docstr_tok: ["A","noun","phrase","extractor","that","uses","chunk","parsing","trained","with","the","ConLL-2000","training","corpus","."]
docstr: Return a list of noun phrases (strings) for body of text.
docstr_tok: ["Return","a","list","of","noun","phrases","(","strings",")","for","body","of","text","."]
code: def extract(self, text):
      """Return a list of noun phrases (strings) for body of text."""
      sentences = nltk.tokenize.sent_tokenize(text)
      noun_phrases = []
      for sentence in sentences:
          parsed = self._parse_sentence(sentence)
          # Get the string representation of each subtree that is a
          # noun phrase tree
          phrases = [
              _normalize_tags(
                  filter_insignificant(
                      each, self.INSIGNIFICANT_SUFFIXES
                  )
              )
              for each in parsed
              if isinstance(each, nltk.tree.Tree)
              and each.label() == "NP"
              and len(filter_insignificant(each)) >= 1
              and _is_match(each, cfg=self.CFG)
          ]
          nps = [tree2str(phrase) for phrase in phrases]
          noun_phrases.extend(nps)
      return noun_phrases
  
imports: from __future__ import unicode_literals, absolute_import
  import nltk
  from textblob.taggers import PatternTagger
  from textblob.decorators import requires_nltk_corpus
  from textblob.utils import tree2str, filter_insignificant
  from textblob.base import BaseNPExtractor
label_desc: Use image node_id 2 for calling the ConllExtractor obj's underlying member method code with example usage: obj.extract(text) and returns: noun_phrases