基於WordNet的相似度的最大分數

Question

某些模擬分數的範圍介於0和1之間，如最短路徑和WuP。因此，汽車與汽車之間的相似性將是1，但其他措施，如LCh中會

lch(car, automobile) = 3.6889 

我想知道這些措施的最大比分。 3.6889是否被認爲是最大值？這是否意味着LCH得分介於0和3.6889之間。

我除了以下措施

jcn(car, automobile) = 12876699.5 
res(car, automobile) = 9.3679 
lesk(car, automobile) = 9519 

Answer 1

似乎3.6375861597263857是lch_similarity最大（我不能得到3.6889 ...）。 lch_similarity，根據the documentation具有以下屬性：

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 is the taxonomy 
     depth. 
... 
:return: A score denoting the similarity of the two ``Synset`` objects, 
      normally greater than 0. None is returned if no connecting path 
      could be found. If a ``Synset`` is compared with itself, the 
      maximum score is returned, which varies depending on the taxonomy 
      depth. 

鑑於rock_hind.n.01是在WordNet的分類程度最深（19），並且change.n.06處於最淺的級（2），我們可以與變化的深度實驗：

>>> from nltk.corpus import wordnet as wn 
>>> rock = wn.synset('rock_hind.n.01') 
>>> change = wn.synset('change.n.06') 
>>> rock.lch_similarity(rock) 
3.6375861597263857 
>>> change.lch_similarity(change) 
3.6375861597263857 
>>> change.lch_similarity(rock) 
0.7472144018302211 
>>> rock.lch_similarity(change) 
0.7472144018302211 

類似的實驗可以爲其他的措施，其中範圍似乎做出了不少大：

>>> from nltk.corpus import wordnet_ic, genesis 
>>> brown_ic = wordnet_ic.ic('ic-brown.dat') 
>>> semcor_ic = wordnet_ic.ic('ic-semcor.dat') 
>>> genesis_ic = wn.ic(genesis, False, 0.0) 
>>> rock.res_similarity(rock, brown_ic) # res_similarity, brown 
1e+300 
>>> rock.res_similarity(change, brown_ic) 
-0.0 
>>> rock.res_similarity(rock, semcor_ic) # res_similarity, semcor 
1e+300 
>>> rock.res_similarity(change, semcor_ic) 
-0.0 
>>> rock.res_similarity(rock, genesis_ic) # res_similarity, genesis 
1e+300 
>>> rock.res_similarity(change, genesis_ic) 
-0.08306855877006339 
>>> change.res_similarity(rock, genesis_ic) 
-0.08306855877006339 
>>> rock.jcn_similarity(rock, brown_ic) # jcn, brown - results are identical with semcor and genesis 
1e+300 
>>> rock.jcn_similarity(change, brown_ic) 
1e-300 
>>> change.jcn_similarity(rock, brown_ic) 
1e-300