anchor.diff:  
   +h(2, "pos-tagging") Part-of-speech Tagging
   
  ++aside("Tip: Understanding tags")
  +    |  You can also use #[code spacy.explain()] to get the escription for the
  +    |  string representation of a tag. For example,
  +    |  #[code spacy.explain("RB")] will return "adverb".
  +
   include _annotation/_pos-tags
   
   +h(2, "lemmatization") Lemmatization
  
positive.code:     |  (#[code ' ']) is included as a token.
  
  +aside-code("Example").
      from spacy.en import English
      nlp = English(parser=False)
      tokens = nlp('Some\nspaces  and\ttab characters')
      print([t.orth_ for t in tokens])
      # ['Some', '\n', 'spaces', ' ', 'and', '\t', 'tab', 'characters']
  
  p
      |  The whitespace tokens are useful for much the same reason punctuation is
  
negative.code:     +item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
      +item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"
  
  p
      |  The lemmatization data is taken from
      |  #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
      |  special case for pronouns: all pronouns are lemmatized to the special
      |  token #[code -PRON-].
  
  +infobox("About spaCy's custom pronoun lemma")
      |  Unlike verbs and common nouns, there's no clear base form of a personal
      |  pronoun. Should the lemma of "me" be "I", or should we normalize person
      |  as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
      |  novel symbol, #[code -PRON-], which is used as the lemma for
      |  all personal pronouns.
  
  +h(2, "dependency-parsing") Syntactic Dependency Parsing
  
  +aside("Tip: Understanding labels")
      |  You can also use #[code spacy.explain()] to get the description for the
      |  string representation of a label. For example,
      |  #[code spacy.explain("prt")] will return "particle".
  
  include _annotation/_dep-labels
  
  +h(2, "named-entities") Named Entity Recognition
  
  +aside("Tip: Understanding entity types")
      |  You can also use #[code spacy.explain()] to get the description for the
      |  string representation of an entity label. For example,
      |  #[code spacy.explain("LANGUAGE")] will return "any named language".
  
  include _annotation/_named-entities
  
  