anchor.diff:          |  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 unlike
  -        |  #[+a("https://github./nltk/nltk") NLTK], which is intended for
  -        |  teaching and research, spaCy follows a more opinionated approach and
  -        |  focuses on production usage. Its aim is to provide you with the best
  -        |  possible general-purpose solution for text processing and machine learning
  -        |  with text input – but this also means that there's only one implementation
  -        |  of each component.
  +        |  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].
  
positive.code: +h(2, "features") Features
  
  p
      |  Across the documentations, 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.
  
  
negative.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. You can always get the offset of a token into the
      |  original string, or reconstruct the original by joining the tokens and their
      |  trailing whitespace. This way, you'll never lose any information
      |  when processing text with spaCy.
  
  +h(3, "annotations-token") Tokenization
  