Extracting nouns alone from NLTK WordNet

Question

Is there any way to detect if a given word is noun or not using nltk wordnet?Also i want to extract the particular word's meaning alone.How to do it?

Answer 1

To detect if a word is a noun or not try this.

from nltk.corpus import wordnet as wn
from nltk.corpus.reader import NOUN

#this gives a synsets list of empty length, since there is no noun corresponding to 'propose'
synsets = wn.synsets('propose', NOUN)

if synsets.length == 0 :
    print ' We found a pure NOUN'

 #this will give you a non empty synset list since 'iron' can be a NOUN too.
synsets = wn.synsets('iron',NOUN)

if synsets.length > 0 :
   print 'Iron is also a noun other than verb'

In order to solve the second part - a word may have many meanings, you would want to define clearly what your meaning is - there are various relations between words such as hypernymy, holonymy, hyponymy, synonym etc.

Also finding a meaning that is closest in meaning to a given word, you may need to find the similarity between a word and each of it's synsets and pick up the one with the highest value. refer to LCH similarity and JCN similarity modules within Wordnet for more information on this