      |  spaCy is able to compare two objects, and make a prediction of
      |  #[strong how similar they are]. Predicting similarity is useful for
      |  building recommendation systems or flagging duplicates. For example, you
      |  can suggest a user content that's similar to what they're currently
      |  looking at, or label a support ticket as a duplicate, if it's very
      |  similar to an already existing one.
  
  p
      |  Each #[code Doc], #[code Span] and #[code Token] comes with a
      |  #[+api("token#similarity") #[code .similarity()]] method that lets you
 
</s> remove     |  and simplified. It's now also possible to overwrite the functions that
    |  compute lexical attributes like #[code like_num], and supply
    |  language-specific syntax iterators, e.g. to determine noun chunks. spaCy
    |  now also supports simple lookup-based lemmatization. The data is stored
    |  in a dictionary mapping a string to its lemma.
 </s> add     |  and simplified. spaCy now also supports simple lookup-based lemmatization.

+image
    include ../../assets/img/docs/language_data.svg </s> remove     assert 'HelloWorld' in matcher
 </s> add     assert 'HEARTS' in matcher </s> remove     |  #[strong add your own entities] to a document and how to train and update
    |  the entity predictions of a model, see the usage guide on
    |  #[+a("/docs/usage/entity-recognition") named entity recognition].
 </s> add     |  #[strong add your own entities] to a document and how to
    |  #[strong train and update] the entity predictions of a model, see the
    |  usage guides on
    |  #[+a("/docs/usage/entity-recognition") named entity recognition] and
    |  #[+a("/docs/usage/training-ner") training the named entity recognizer]. </s> remove         +cell #[code Tokenizer.load]
        +cell
            |  #[+api("tokenizer#from_disk") #[code Tokenizer.from_disk]]
            |  #[+api("tokenizer#from_bytes") #[code Tokenizer.from_bytes]]

    +row
        +cell #[code Tagger.load]
        +cell
            |  #[+api("tagger#from_disk") #[code Tagger.from_disk]]
            |  #[+api("tagger#from_bytes") #[code Tagger.from_bytes]]

    +row
        +cell #[code DependencyParser.load]
        +cell
            |  #[+api("dependencyparser#from_disk") #[code DependencyParser.from_disk]]
            |  #[+api("dependencyparser#from_bytes") #[code DependencyParser.from_bytes]]

    +row
        +cell #[code EntityRecognizer.load]
        +cell
            |  #[+api("entityrecognizer#from_disk") #[code EntityRecognizer.from_disk]]
            |  #[+api("entityrecognizer#from_bytes") #[code EntityRecognizer.from_bytes]]
 </s> add         +cell #[code Language.create_make_doc]
        +cell #[+api("language#attributes") #[code Language.tokenizer]] </s> remove         +cell #[coce Vocab]
 </s> add         +cell #[code Vocab]
    |  looking at, or label a support ticket as a duplicate if it's very
      |  spaCy is able to compare two objects, and make a prediction of
      |  #[strong how similar they are]. Predicting similarity is useful for
      |  building recommendation systems or flagging duplicates. For example, you
      |  can suggest a user content that's similar to what they're currently
    |  looking at, or label a support ticket as a duplicate if it's very
      |  similar to an already existing one.
  
  p
      |  Each #[code Doc], #[code Span] and #[code Token] comes with a
      |  #[+api("token#similarity") #[code .similarity()]] method that lets you
keep keep keep keep replace keep keep keep keep keep
 Fix typos, wording and formatting 
website/docs/usage/_spacy-101/_similarity.jade