code: def lossy_tokenize(text, lang, include_punctuation=False, external_wordlist=False):
      
      global _simplify_chinese
  
      info = get_language_info(lang)
      tokens = tokenize(text, lang, include_punctuation, external_wordlist)
  
      if info['lookup_transliteration'] == 'zh-Hans':
          from wordfreq.chinese import simplify_chinese as _simplify_chinese
          tokens = [_simplify_chinese(token) for token in tokens]
  
      return [smash_numbers(token) for token in tokens]
docs: Get a list of tokens for this text, with largely the same results and
      options as `tokenize`, but aggressively normalize some text in a lossy way
      that's good for counting word frequencies.
  
      In particular:
  
      - Any sequence of 2 or more adjacent digits, possibly with intervening
        punctuation such as a decimal point, will replace each digit with '0'
        so that frequencies for numbers don't have to be counted separately.
  
        This is similar to but not quite identical to the word2vec Google News
        data, which replaces digits with '#' in tokens with more than one digit.
  
      - In Chinese, unless Traditional Chinese is specifically requested using
        'zh-Hant', all characters will be converted to Simplified Chinese.