class_docstr: A fast and simple noun phrase extractor.
  
  Credit to Shlomi Babluk. Link to original blog post:
  
      http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/
class_docstr_tok: ["A","fast","and","simple","noun","phrase","extractor",".","Credit","to","Shlomi","Babluk",".","Link","to","original","blog","post",":","http",":","\/\/thetokenizer.com\/2013\/05\/09\/efficient-way-to-extract-the-main-topics-of-a-sentence\/"]
code: def train(self):
      train_data = nltk.corpus.brown.tagged_sents(categories="news")
      regexp_tagger = nltk.RegexpTagger(
          [
              (r"^-?[0-9]+(.[0-9]+)?$", "CD"),
              (r"(-|:|;)$", ":"),
              (r"\'*$", "MD"),
              (r"(The|the|A|a|An|an)$", "AT"),
              (r".*able$", "JJ"),
              (r"^[A-Z].*$", "NNP"),
              (r".*ness$", "NN"),
              (r".*ly$", "RB"),
              (r".*s$", "NNS"),
              (r".*ing$", "VBG"),
              (r".*ed$", "VBD"),
              (r".*", "NN"),
          ]
      )
      unigram_tagger = nltk.UnigramTagger(
          train_data, backoff=regexp_tagger
      )
      self.tagger = nltk.BigramTagger(
          train_data, backoff=unigram_tagger
      )
      self._trained = True
      return None
  
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 FastNPExtractor obj's underlying member method code with example usage: obj.train() and returns: None