anchor.diff:  +code.
       nlp.tokenizer.add_special_case(u'...gimme...?',
           [{
  -            ORTH: u'...gimme...?', LEMMA: u'give', TAG: u'VB'}])
  +            ORTH: u'...gimme...?', LEMMA: u'give', POS: u'VB'}])
       assert len(nlp(u'...gimme...?')) == 1
   
   p
  
positive.code:             {
                  ORTH: u'me'}])
      assert [w.text for w in nlp(u'gimme that')] == [u'gim', u'me', u'that']
      assert [w.lemma_ for w in nlp(u'gimme that')] == [u'give', u'me', u'that']
  
  p
      |  The special case doesn't have to match an entire whitespace-delimited
  
negative.code: 
      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, rules={},
                  prefix_search=prefix_re.search,
                  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
  