anchor.diff:      |  are overwritten.
   
   +h(3, "lemmatizer") Lemmatizer
  +
  +p
  +    |  As of v2.0, spaCy supports simple lookup-based lemmatization. This is
  +    |  usually the quickest and easiest way to get started. The data is stored
  +    |  in a dictionary mapping a string to its lemma. To determine a token's
  +    |  lemma, spaCy simply looks it up in the table. Here's an example from
  +    |  the Spanish language data:
  +
  ++code("lang/es/lemmatizer.py (excerpt)").
  +    LOOKUP = {
  +        "aba": "abar",
  +        "ababa": "abar",
  +        "ababais": "abar",
  +        "ababan": "abar",
  +        "ababanes": "ababán",
  +        "ababas": "abar",
  +        "ababoles": "ababol",
  +        "ababábites": "ababábite"
  +    }
  +
  ++aside("Where can I find lemmatizer data?")
  +
  +p
  +    |  To add a lookup lemmatizer to your language, import the #[code LOOKUP]
  +    |  table and #[code Lemmatizer], and create a new classmethod:
  +
  +
  ++code("__init__py (excerpt)").
  +    # other imports here, plus lookup table and lookup lemmatizer
  +    from .lemmatizer import LOOKUP
  +    from ...lemmatizerlookup import Lemmatizer
  +
  +    class Xxxxx(Language):
  +        lang = 'xx'
  +
  +        class Defaults(Language.Defaults):
  +            # other language defaults here
  +
  +            @classmethod
  +            def create_lemmatizer(cls, nlp=None):
  +                return Lemmatizer(LOOKUP)
   
   +h(3, "tag-map") Tag map
   
  
positive.code:     |  newlines, and added as a multiline string.
  
  +aside("What does spaCy consider a stop word?")
      |  There's no particularly principal logic behind what words should be
      |  added to the stop list. Make a list that you think might be useful
      |  to people and is likely to be unsurprising. As a rule of thumb, words
      |  that are very rare are unlikely to be useful stop words.
  
negative.code:     +row
          +cell #[+src(gh()) morph_rules.py]
          +cell #[code MORPH_RULES] (dict)
          +cell Exception rules for morphological analysis of irregular words.
  
      +row
          +cell #[+src(gh("spacy-dev-resources", "templates/new_language/lemmatizer.py")) lemmatizer.py]
          +cell #[code LOOKUP] (dict)
          +cell
              |  Lookup-based lemmatization table. If more lemmatizer data is
  