I am trying to solve a nlp problem where i have a dict of words like :
list_1={'phone':'android','chair':'netflit','charger':'macbook','laptop','sony'}
Now if input is 'phone' i can easily use 'in' operator to get the description of phone and its data by key but problem is if input is something like 'phones' or 'Phones' .
I want if i input 'phone' then i get words like
'phone' ==> 'Phones','phones','Phone','Phone's','phone's'
I don't know which word2vec i can use and which nlp module can provide solution like this.
second issue is if i give a word 'Dog' can i get words like 'Puppy','Kitty','Dog','dog' etc ?
I tried something like this but its giving synonyms :
from nltk.corpus import wordnet as wn
for ss in wn.synsets('phone'): # Each synset represents a diff concept.
print(ss)
but its returning :
Synset('telephone.n.01')
Synset('phone.n.02')
Synset('earphone.n.01')
Synset('call.v.03')
Instead i wanted :
'phone' ==> 'Phones','phones','Phone','Phone's','phone's'