code: def adapted_lesk(context_sentence: str, ambiguous_word: str,
                  pos: str = None, lemma=True, stem=False, hyperhypo=True,
                  stop=True, context_is_lemmatized=False,
                  nbest=False, keepscore=False, normalizescore=False,
                  from_cache=True) -> "wn.Synset":
      
  
      # Ensure that ambiguous word is a lemma.
      ambiguous_word = lemmatize(ambiguous_word)
      # If ambiguous word not in WordNet return None
      if not wn.synsets(ambiguous_word):
          return None
      # Get the signatures for each synset.
      ss_sign = signatures(ambiguous_word, pos=pos, hyperhypo=hyperhypo, adapted=True,
                           remove_stopwords=stop, to_lemmatize=lemma,
                           remove_numbers=True, lowercase=True, to_stem=stem,
                           from_cache=from_cache)
  
      # Disambiguate the sense in context.
      context_sentence = context_sentence.split() if context_is_lemmatized else lemmatize_sentence(context_sentence)
      return compare_overlaps(context_sentence, ss_sign, nbest=nbest,
                              keepscore=keepscore, normalizescore=normalizescore)
docs: This function is the implementation of the Adapted Lesk algorithm,
      described in Banerjee and Pederson (2002). It makes use of the lexical
      items from semantically related senses within the wordnet
      hierarchies and to generate more lexical items for each sense.
      see www.d.umn.edu/~tpederse/Pubs/cicling2002-b.pdf‎
  
      :param context_sentence: String, sentence or document.
      :param ambiguous_word: String, a single word.
      :param pos: String, one of 'a', 'r', 's', 'n', 'v', or None.
      :return: A Synset for the estimated best sense.