docs: def clean_corpus_serial(corpus, lemmatizing="wordnet"):
      """
      Extracts a bag-of-words from each document in a corpus serially.
  
      Inputs: - corpus: A python list of python strings. Each string is a document.
              - lemmatizing: A string containing one of the following: "porter", "snowball" or "wordnet".
  
      Output: - list_of_bags_of_words: A list of python dictionaries representing bags-of-words.
              - lemma_to_keywordbag_total: Aggregated python dictionary that maps stems/lemmas to original topic keywords.
      """
      list_of_bags_of_words = list()
      append_bag_of_words = list_of_bags_of_words.append
  
      lemma_to_keywordbag_total = defaultdict(lambda: defaultdict(int))
  
      for document in corpus:
          word_list, lemma_to_keywordbag = clean_document(document=document, lemmatizing=lemmatizing)  # TODO: Alter this.
          bag_of_words = combine_word_list(word_list)
          append_bag_of_words(bag_of_words)
  
          for lemma, keywordbag in lemma_to_keywordbag.items():
              for keyword, multiplicity in keywordbag.items():
                  lemma_to_keywordbag_total[lemma][keyword] += multiplicity
  
      return list_of_bags_of_words, lemma_to_keywordbag_total