近日，部分pywsd代码已被移植到前沿版本NLTK' 在里面wsd.py模块，尝试：
>>> from nltk.wsd import lesk
>>> sent = 'I went to the bank to deposit my money'
>>> ambiguous = 'bank'
>>> lesk(sent, ambiguous)
Synset('bank.v.04')
>>> lesk(sent, ambiguous).definition()
u'act as the banker in a game or in gambling'
为了获得更好的 WSD 性能，请使用pywsd图书馆而不是NLTK模块。一般来说，simple_lesk() from pywsd比lesk from NLTK。我会尝试更新NLTK当我有空的时候，尽可能多地使用模块。
回应克里斯·斯宾塞的评论，请注意Lesk算法的局限性。我只是给出算法的准确实现。这不是灵丹妙药http://en.wikipedia.org/wiki/Lesk_algorithm http://en.wikipedia.org/wiki/Lesk_algorithm
另请注意，尽管：
lesk("My cat likes to eat mice.", "cat", "n")
没有给你正确的答案，你可以使用pywsd实施max_similarity():
>>> from pywsd.similarity import max_similiarity
>>> max_similarity('my cat likes to eat mice', 'cat', 'wup', pos='n').definition
'feline mammal usually having thick soft fur and no ability to roar: domestic cats; wildcats'
>>> max_similarity('my cat likes to eat mice', 'cat', 'lin', pos='n').definition
'feline mammal usually having thick soft fur and no ability to roar: domestic cats; wildcats'
@Chris，如果你想要一个 python setup.py ，只需做一个礼貌的请求，我会写它......
