python-3.x - wordnet python-nltk 接口(interface)是否包含任何语义相关性度量？

Question

我知道我可以使用 nltk 接口(interface)中的语义相似性

sim=wn.synset(name_1).path_similarity(wn.synset(name_2))

我也知道我可以使用向量空间模型和共现矩阵来评估单词的语义相关性，但我无法在 nltk 界面中找到任何解决方案。

Answer 1

NLTK-WordNet 拥有大量基于 WordNet 分类法的单词相似度算法，尽管没有一个算法基于向量空间模型或共现矩阵。

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