wordnet python-nltk 接口是否包含任何语义相关性度量？
Does wordnet python-nltk interface includes any measure of semantic relatedness?
我知道我可以在 nltk 接口中使用语义相似度
sim=wn.synset(name_1).path_similarity(wn.synset(name_2))
我也知道我可以使用向量 space 模型和共现矩阵来评估单词的语义相关性，但我无法在 nltk 接口中找到任何解决方案。
NLTK-WordNet 有许多基于 WordNet 分类法的词相似度算法，尽管 none 是基于向量 space 模型或 co-occurrence 矩阵。
from nltk.corpus import wordnet as wn
from nltk.corpus import wordnet_ic
# Wordnet information content file
brown_ic = wordnet_ic.ic('ic-brown.dat')
cat = wn.synsets('cat')[0]
dog = wn.synsets('dog')[0]
'''
Path Similarity:
Return a score denoting how similar two word senses are,
based on the shortest path that connects the senses
in the is-a (hypernym/hypnoym) taxonomy.
The score is in the range 0 to 1.
'''
print(wn.path_similarity(cat, dog))
# 0.2
'''
Leacock-Chodorow Similarity:
Return a score denoting how similar two word senses are,
based on the shortest path that connects the senses (as above)
and the maximum depth of the taxonomy in which the senses occur.
The relationship is given as -log(p/2d)
where p is the shortest path length and d the taxonomy depth.
'''
print(wn.lch_similarity(cat, dog))
# 2.0281482472922856
'''
Wu-Palmer Similarity:
Return a score denoting how similar two word senses are,
based on the depth of the two senses in the taxonomy
and that of their Least Common Subsumer (most specific ancestor node).
'''
print(wn.wup_similarity(cat, dog))
# 0.8571428571428571
'''
Lin Similarity:
Return a score denoting how similar two word senses are,
based on the Information Content (IC) of the Least Common Subsumer
and that of the two input Synsets.
The relationship is given by the equation 2 * IC(lcs) / (IC(s1) + IC(s2)).
'''
print(wn.lin_similarity(cat, dog, ic=brown_ic))
# 0.8768009843733973
'''
Resnik Similarity:
Return a score denoting how similar two word senses are,
based on the Information Content (IC) of the Least Common Subsumer
Note that for any similarity measure that uses information content,
the result is dependent on the corpus used to generate the information content
and the specifics of how the information content was created.
'''
print(wn.res_similarity(cat, dog, ic=brown_ic))
# 7.911666509036577
'''
Jiang-Conrath Similarity
Return a score denoting how similar two word senses are,
based on the Information Content (IC) of the Least Common Subsumer
and that of the two input Synsets.
The relationship is given by the equation 1 / (IC(s1) + IC(s2) - 2 * IC(lcs)).
'''
print(wn.jcn_similarity(cat, dog, ic=brown_ic))
# 0.4497755285516739
我知道我可以在 nltk 接口中使用语义相似度
sim=wn.synset(name_1).path_similarity(wn.synset(name_2))
我也知道我可以使用向量 space 模型和共现矩阵来评估单词的语义相关性，但我无法在 nltk 接口中找到任何解决方案。
NLTK-WordNet 有许多基于 WordNet 分类法的词相似度算法，尽管 none 是基于向量 space 模型或 co-occurrence 矩阵。
from nltk.corpus import wordnet as wn
from nltk.corpus import wordnet_ic
# Wordnet information content file
brown_ic = wordnet_ic.ic('ic-brown.dat')
cat = wn.synsets('cat')[0]
dog = wn.synsets('dog')[0]
'''
Path Similarity:
Return a score denoting how similar two word senses are,
based on the shortest path that connects the senses
in the is-a (hypernym/hypnoym) taxonomy.
The score is in the range 0 to 1.
'''
print(wn.path_similarity(cat, dog))
# 0.2
'''
Leacock-Chodorow Similarity:
Return a score denoting how similar two word senses are,
based on the shortest path that connects the senses (as above)
and the maximum depth of the taxonomy in which the senses occur.
The relationship is given as -log(p/2d)
where p is the shortest path length and d the taxonomy depth.
'''
print(wn.lch_similarity(cat, dog))
# 2.0281482472922856
'''
Wu-Palmer Similarity:
Return a score denoting how similar two word senses are,
based on the depth of the two senses in the taxonomy
and that of their Least Common Subsumer (most specific ancestor node).
'''
print(wn.wup_similarity(cat, dog))
# 0.8571428571428571
'''
Lin Similarity:
Return a score denoting how similar two word senses are,
based on the Information Content (IC) of the Least Common Subsumer
and that of the two input Synsets.
The relationship is given by the equation 2 * IC(lcs) / (IC(s1) + IC(s2)).
'''
print(wn.lin_similarity(cat, dog, ic=brown_ic))
# 0.8768009843733973
'''
Resnik Similarity:
Return a score denoting how similar two word senses are,
based on the Information Content (IC) of the Least Common Subsumer
Note that for any similarity measure that uses information content,
the result is dependent on the corpus used to generate the information content
and the specifics of how the information content was created.
'''
print(wn.res_similarity(cat, dog, ic=brown_ic))
# 7.911666509036577
'''
Jiang-Conrath Similarity
Return a score denoting how similar two word senses are,
based on the Information Content (IC) of the Least Common Subsumer
and that of the two input Synsets.
The relationship is given by the equation 1 / (IC(s1) + IC(s2) - 2 * IC(lcs)).
'''
print(wn.jcn_similarity(cat, dog, ic=brown_ic))
# 0.4497755285516739