docs: def _handle_universal(self, X):
          """
          Scan through the corpus to compute counts of each Universal
          Dependencies part-of-speech.
  
          Parameters
          ----------
          X : list or generator
              Should be provided as a list of documents or a generator
              that yields a list of documents that contain a list of
              sentences that contain (token, tag) tuples.
          """
          jump = {
              # combine proper and regular nouns
              "NOUN": "noun", "PROPN": "noun",
              "ADJ": "adjective",
              "VERB": "verb",
              # include particles with adverbs
              "ADV": "adverb", "PART": "adverb",
              "ADP": "adposition",
              "PRON": "pronoun",
              "CCONJ": "conjunction",
              "PUNCT": "punctuation",
              "DET": "determiner",
              "NUM": "number",
              "INTJ": "interjection",
              "SYM": "symbol",
          }
  
          for tagged_doc in X:
              for tagged_sent in tagged_doc:
                  for _, tag in tagged_sent:
                      if tag == "SPACE":
                          continue
                      self.pos_tag_counts_[jump.get(tag, "other")] += 1