AttributeError：'unicode‘对象没有属性'wup_similarity’

Question

我正在使用Python2.7中的nltk模块。以下是我的代码

from nltk.corpus import wordnet as wn

listsyn1 = []
listsyn2 = []

for synset in wn.synsets('dog', pos=wn.NOUN):
    print synset.name()
    for lemma in synset.lemmas():
        listsyn1.append(lemma.name())

for synset in wn.synsets('paw', pos=wn.NOUN):
    print synset.name()
    for lemma in synset.lemmas():
        listsyn2.append(lemma.name())

countsyn1 = len(listsyn1)
countsyn2 = len(listsyn2)

sumofsimilarity = 0;
for firstgroup in listsyn1:
    for secondgroup in listsyn2:
        print(firstgroup.wup_similarity(secondgroup))
        sumofsimilarity = sumofsimilarity + firstgroup.wup_similarity(secondgroup)

averageofsimilarity = sumofsimilarity/(countsyn1*countsyn2)

当我试图运行这段代码时，我得到了错误"AttributeError：'unicode‘对象没有属性'wup_similarity'“。谢谢你的帮助。

Answer 1

相似性度量只能由Synset对象访问，而不能由Lemma或lemma_names (即str类型)访问。

dog = wn.synsets('dog', 'n')[0]
paw = wn.synsets('paw', 'n')[0]

print(type(dog), type(paw), dog.wup_similarity(paw))

输出

<class 'nltk.corpus.reader.wordnet.Synset'> <class 'nltk.corpus.reader.wordnet.Synset'> 0.21052631578947367

当您获得.lemmas()并从Synset对象访问.names()属性时，您将得到str

dog = wn.synsets('dog', 'n')[0]
print(type(dog), dog)
print(type(dog.lemmas()[0]), dog.lemmas()[0])
print(type(dog.lemmas()[0].name()), dog.lemmas()[0].name())

输出

<class 'nltk.corpus.reader.wordnet.Synset'> Synset('dog.n.01')
<class 'nltk.corpus.reader.wordnet.Lemma'> Lemma('dog.n.01.dog')
<class 'str'> dog

您可以使用hasattr函数来检查哪些对象/类型可以访问某个函数或属性：

dog = wn.synsets('dog', 'n')[0]
print(hasattr(dog, 'wup_similarity'))
print(hasattr(dog.lemmas()[0], 'wup_similarity'))
print(hasattr(dog.lemmas()[0].name(), 'wup_similarity'))

输出

True
False
False

最有可能的是，您需要一个类似于https://github.com/alvations/pywsd/blob/master/pywsd/similarity.py#L76的函数，它使两个同步集的wup_similarity最大化，但请注意，有许多必要的警告，比如预柠檬化。

所以我认为这就是你想通过使用.lemma_names()来避免它的地方。也许，你可以这样做：

def ss_lnames(word):
    return set(chain(*[ss.lemma_names() for ss in wn.synsets(word, 'n')]))

dog_lnames = ss_lnames('dog')
paw_lnames = ss_lnames('paw')

for dog_name, paw_name in product(dog_lnames, paw_lnames):
    for dog_ss, paw_ss in product(wn.synsets(dog_name, 'n'), wn.synsets(paw_name, 'n')):
        print(dog_ss, paw_ss, dog_ss.wup_similarity(paw_ss))  

但最有可能的结果是不可解释和不可靠的，因为没有词义消歧在同步之前上升，在外部和内部循环中查找机器人。