anchor.diff:  
       prefix_re = re.compile(r'''[\[\(&quot;']''')
       suffix_re = re.compile(r'''[\]\)&quot;']''')
  +    infix_re = re.compile(r'''[-~]''')
       def create_tokenizer(nlp):
  -        return Tokenizer(nlp.vocab,
  +        return Tokenizer(nlp.vocab, rules={},
                   prefix_search=prefix_re.search,
  -                suffix_search=suffix_re.search)
  -
  -    nlp = spacy.load('en', tokenizer=create_make_doc)
  +                suffix_search=suffix_re.search,
  +                infix_finditer=infix_re.finditer)
  +
  +    nlp = spacy.load('en', create_make_doc=create_tokenizer)
   
   p
       |  If you need to subclass the tokenizer instead, the relevant methods to
  
positive.code: +code.
      nlp.tokenizer.add_special_case(u'...gimme...?',
          [{
              ORTH: u'...gimme...?', LEMMA: u'give', TAG: u'VB'}])
      assert len(nlp(u'...gimme...?')) == 1
  
  p
  
negative.code:             {
                  ORTH: u'me'}])
      assert [w.text for w in nlp(u'gimme that')] == [u'gim', u'me', u'that']
      # Pronoun lemma is returned as -PRON-
      # More details please see: https://spacy.io/docs/usage/troubleshooting#pron-lemma
      assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'-PRON-', u'that']
  
  p
      |  The special case doesn't have to match an entire whitespace-delimited
  