code: def clean_text_by_word(text, language="english", deacc=False, additional_stopwords=None):
      
      init_textcleanner(language, additional_stopwords)
      text_without_acronyms = replace_with_separator(text, "", [AB_ACRONYM_LETTERS])
      original_words = list(tokenize(text_without_acronyms, lowercase=True, deacc=deacc))
      filtered_words = filter_words(original_words)
      if HAS_PATTERN:
          tags = tag(" ".join(original_words))  # tag needs the context of the words in the text
      else:
          tags = None
      units = merge_syntactic_units(original_words, filtered_words, tags)
      return { unit.text : unit for unit in units }
docs: Tokenizes a given text into words, applying filters and lemmatizing them.
      Returns a dict of word -> syntacticUnit.