code: def textrank(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False):
          
          self.pos_filt = frozenset(allowPOS)
          g = UndirectWeightedGraph()
          cm = defaultdict(int)
          words = tuple(self.tokenizer.cut(sentence))
          for i, wp in enumerate(words):
              if self.pairfilter(wp):
                  for j in xrange(i + 1, i + self.span):
                      if j >= len(words):
                          break
                      if not self.pairfilter(words[j]):
                          continue
                      if allowPOS and withFlag:
                          cm[(wp, words[j])] += 1
                      else:
                          cm[(wp.word, words[j].word)] += 1
  
          for terms, w in cm.items():
              g.addEdge(terms[0], terms[1], w)
          nodes_rank = g.rank()
          if withWeight:
              tags = sorted(nodes_rank.items(), key=itemgetter(1), reverse=True)
          else:
              tags = sorted(nodes_rank, key=nodes_rank.__getitem__, reverse=True)
  
          if topK:
              return tags[:topK]
          else:
              return tags
docs: Extract keywords from sentence using TextRank algorithm.
          Parameter:
              - topK: return how many top keywords. `None` for all possible words.
              - withWeight: if True, return a list of (word, weight);
                            if False, return a list of words.
              - allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v'].
                          if the POS of w is not in this list, it will be filtered.
              - withFlag: if True, return a list of pair(word, weight) like posseg.cut
                          if False, return a list of words