这是一种启发式方法。我刚刚对其进行了编码，以便为样式使用代码。它使用来自wordnet的derivationally_related_forms（）。我已经实现名词化。我猜verbify工作类似。根据我的测试，效果很好：
from nltk.corpus import wordnet as wndef 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
