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from nltk.corpus import wordnet as wn

def nounify(verb_word):
    """ Transform a verb to the closest noun: die -> death """
    verb_synsets = wn.synsets(verb_word, pos="v")

    # Word not found
    if not verb_synsets:
        return []

    # Get all verb lemmas of the word
    verb_lemmas = [l for s in verb_synsets for l in s.lemmas() if (s.name()).split('.')[1] == 'v']

    # Get related forms
    derivationally_related_forms = [(l, l.derivationally_related_forms()) for l in  verb_lemmas]

    # filter only the nouns
    related_noun_lemmas = [l for drf in derivationally_related_forms for l in drf[1] if l.synset().name().split('.')[1] == 'n']

    # Extract the words from the lemmas
    words = [l.name for l in related_noun_lemmas]
    len_words = len(words)

    # Build the result in the form of a list containing tuples (word, probability)
    result = [(w, float(words.count(w))/len_words) for w in set(words)]
    result.sort(key=lambda w: -w[1])

    # return all the possibilities sorted by probability
    return result



if __name__ == '__main__':

    print nounify("sprechen")