anchor.diff:      |  assigned to each token in the document. They're useful in rule-based
       |  processes. They can also be useful features in some statistical models.
   
  -p
  -    |  To use spaCy's tagger, you need to have a data pack installed that
  -    |  includes a tagging model. Tagging models are included in the data
  -    |  downloads for English and German. After you load the model, the tagger
  -    |  is applied automatically, as part of the default pipeline. You can then
  -    |  access the tags using the #[+api("token") #[code Token.tag]] and
  -    |  #[+api("token") #[code token.pos]] attributes. For English, the tagger
  -    |  also triggers some simple rule-based morphological processing, which
  -    |  gives you the lemma as well.
  ++h(2, "101") Part-of-speech tagging 101
  +    +tag-model("dependency parse")
   
  -+code("Usage").
  -    import spacy
  -    nlp = spacy.load('en')
  -    doc = nlp(u'They told us to duck.')
  -    for word in doc:
  -        print(word.text, word.lemma, word.lemma_, word.tag, word.tag_, word.pos, word.pos_)
  +include _spacy-101/_pos-deps
  +
  ++aside("Help – spaCy's output is wrong!")
   
   +h(2, "rule-based-morphology") Rule-based morphology
   
  
positive.code: 
  +list("numbers")
      +item
          |  The tokenizer consults a #[strong mapping table]
          |  #[code TOKENIZER_EXCEPTIONS], which allows sequences of characters
          |  to be mapped to multiple tokens. Each token may be assigned a part
          |  of speech and one or more morphological features.
  
negative.code: 
      +item
          |  For words whose POS is not set by a prior process, a
          |  #[+a("/docs/usage/adding-languages#tag-map") mapping table]
          |  #[code TAG_MAP] maps the tags to a part-of-speech and a set of
          |  morphological features.
  
      +item
          |  Finally, a #[strong rule-based deterministic lemmatizer] maps the
  