In [8]:

    

from nltk.corpus import wordnet as wn
from itertools import product

wordx, wordy = "phone","tablet"
sem1, sem2 = wn.synsets(wordx), wn.synsets(wordy)


    

In [9]:

    

sem1


    

    
Out[9]:





[Synset('telephone.n.01'),
 Synset('phone.n.02'),
 Synset('earphone.n.01'),
 Synset('call.v.03')]

In [10]:

    

sem2


    

    
Out[10]:





[Synset('tablet.n.01'),
 Synset('pad.n.01'),
 Synset('tablet.n.03'),
 Synset('pill.n.02')]

In [11]:

    

prod = list(product(*[sem1,sem2]))


    

In [12]:

    

prod[1][0].wup_similarity(prod[1][1])


    

    
Out[12]:





0.25

In [13]:

    

maxscore = 0.0
for i,j in prod:
    score = i.wup_similarity(j) # Wu-Palmer Similarity
    if score is not None:
        if maxscore < score:
            maxscore = score


    

In [15]:

    

print("Similarity between phone and tablet is : " + str(maxscore))


    

    




Similarity between phone and tablet is : 0.5882352941176471

In [3]:

    

import nltk

text = nltk.Text(word.lower() for word in nltk.corpus.brown.words())
text.similar('woman')


    

    




man time day year car moment world house family child country boy
state job place way war girl work word

In [4]:

    

similar_words = text._word_context_index.similar_words('woman')
print(' '.join(similar_words))


    

    




man number time world fact end year state house way day use part kind boy matter problem result girl group

In [5]:

    

text._word_context_index.common_contexts(['cat', 'dog'])


    

    
Out[5]:





FreqDist({('a', 'a'): 2, ('a', 'he'): 2, ('the', 'and'): 2})

In [6]:

    

len(text._word_context_index.common_contexts(['brown', 'red']))


    

    
Out[6]:





3

In [7]:

    

text


    

    
Out[7]:





<Text: the fulton county grand jury said friday an...>

In [17]:

    

x = {}
x["hi"] = "there"


    

In [18]:

    

x


    

    
Out[18]:





{'hi': 'there'}

In [20]:

    

"there" in x


    

    
Out[20]:





False

In [ ]: