anchor.diff:  +h(2, "features") Features
   
   p
  -    |  Across the documentations, you'll come across mentions of spaCy's
  +    |  Across the documentation, you'll come across mentions of spaCy's
       |  features and capabilities. Some of them refer to linguistic concepts,
       |  while others are related to more general machine learning functionality.
   
  
positive.code: p
      |  Even though a #[code Doc] is processed – e.g. split into individual words
      |  and annotated – it still holds #[strong all information of the original text],
      |  like whitespace characters. This way, you'll never lose any information
      |  when processing text with spaCy.
  
  +h(3, "annotations-token") Tokenization
  
negative.code:         |  not designed specifically for chat bots, and only provides the
          |  underlying text processing capabilities.
      +item #[strong spaCy is not research software].
          |  It's is built on the latest research, but it's designed to get
          |  things done. This leads to fairly different design decisions than
          |  #[+a("https://github./nltk/nltk") NLTK]
          |  or #[+a("https://stanfordnlp.github.io/CorenlP") CoreNLP], which were
          |  created as platforms for teaching and research.  The main difference
          |  is that spaCy is integrated and opinionated. We try to avoid asking
          |  the user to choose between multiple algorithms that deliver equivalent
          |  functionality.  Keeping our menu small lets us deliver generally better
          |  performance and developer experience.
      +item #[strong spaCy is not a company].
          |  It's an open-source library. Our company publishing spaCy and other
          |  software is called #[+a(COMPANY_URL, true) Explosion AI].
  