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\/"]
docstr: Split the sentence into single words/tokens
docstr_tok: ["Split","the","sentence","into","single","words\/tokens"]
code: def _tokenize_sentence(self, sentence):
      """Split the sentence into single words/tokens"""
      tokens = nltk.word_tokenize(sentence)
      return tokens
  
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 3 for calling the FastNPExtractor obj's underlying member method code with example usage: obj._tokenize_sentence(sentence) and returns: tokens