If you're going to use WordNet, you have
Problem 1: Word Sense Disambiguation (WSD), i.e. how to automatically determine which synset to use?
>>> for i in wn.synsets('good','a'):
... print i.name, i.definition
...
good.a.01 having desirable or positive qualities especially those suitable for a thing specified
full.s.06 having the normally expected amount
good.a.03 morally admirable
estimable.s.02 deserving of esteem and respect
beneficial.s.01 promoting or enhancing well-being
good.s.06 agreeable or pleasing
good.s.07 of moral excellence
adept.s.01 having or showing knowledge and skill and aptitude
good.s.09 thorough
dear.s.02 with or in a close or intimate relationship
dependable.s.04 financially sound
good.s.12 most suitable or right for a particular purpose
good.s.13 resulting favorably
effective.s.04 exerting force or influence
good.s.15 capable of pleasing
good.s.16 appealing to the mind
good.s.17 in excellent physical condition
good.s.18 tending to promote physical well-being; beneficial to health
good.s.19 not forged
good.s.20 not left to spoil
good.s.21 generally admired
>>> for i in wn.synsets('great','a'):
... print i.name, i.definition
...
great.s.01 relatively large in size or number or extent; larger than others of its kind
great.s.02 of major significance or importance
great.s.03 remarkable or out of the ordinary in degree or magnitude or effect
bang-up.s.01 very good
capital.s.03 uppercase
big.s.13 in an advanced stage of pregnancy
Let's say you somehow get the correct sense, maybe you tried something like this (https://github.com/alvations/pywsd) and let's say you get the POS and synset right:
good.a.01 having desirable or positive qualities especially those
suitable for a thing specified
great.s.01 relatively large in size or number or extent; larger than others of its kind
Problem 2: How are you going to compare the 2 synsets?
Let's try similarity functions, but you realized that they give you no score:
>>> good = wn.synsets('good','a')[0]
>>> great = wn.synsets('great','a')[0]
>>> print max(wn.path_similarity(good,great), wn.path_similarity(great, good))
None
>>> print max(wn.wup_similarity(good,great), wn.wup_similarity(great, good))
>>> print max(wn.res_similarity(good,great,semcor_ic), wn.res_similarity(great, good,semcor_ic))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1312, in res_similarity
return synset1.res_similarity(synset2, ic, verbose)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 738, in res_similarity
ic1, ic2, lcs_ic = _lcs_ic(self, other, ic)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1643, in _lcs_ic
(synset1, synset2))
nltk.corpus.reader.wordnet.WordNetError: Computing the least common subsumer requires Synset('good.a.01') and Synset('great.s.01') to have the same part of speech.
>>> print max(wn.jcn_similarity(good,great,semcor_ic), wn.jcn_similarity(great, good,semcor_ic))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1316, in jcn_similarity
return synset1.jcn_similarity(synset2, ic, verbose)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 759, in jcn_similarity
ic1, ic2, lcs_ic = _lcs_ic(self, other, ic)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1643, in _lcs_ic
(synset1, synset2))
nltk.corpus.reader.wordnet.WordNetError: Computing the least common subsumer requires Synset('good.a.01') and Synset('great.s.01') to have the same part of speech.
>>> print max(wn.lin_similarity(good,great,semcor_ic), wn.lin_similarity(great, good,semcor_ic))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1320, in lin_similarity
return synset1.lin_similarity(synset2, ic, verbose)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 789, in lin_similarity
ic1, ic2, lcs_ic = _lcs_ic(self, other, ic)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1643, in _lcs_ic
(synset1, synset2))
nltk.corpus.reader.wordnet.WordNetError: Computing the least common subsumer requires Synset('good.a.01') and Synset('great.s.01') to have the same part of speech.
>>> print max(wn.lch_similarity(good,great), wn.lch_similarity(great, good))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1304, in lch_similarity
return synset1.lch_similarity(synset2, verbose, simulate_root)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 638, in lch_similarity
(self, other))
nltk.corpus.reader.wordnet.WordNetError: Computing the lch similarity requires Synset('good.a.01') and Synset('great.s.01') to have the same part of speech.
Let's try a different pair of synsets, since good has both satellite-adjective and adjective while great only have satellite, let's go with the lowest common denominator:
good.s.13 resulting favorably
great.s.01 relatively large in size or number or extent; larger than others of its kind
You realize that there is still no similarity information for comparing between satellite-adjective:
>>> print max(wn.lin_similarity(good,great,semcor_ic), wn.lin_similarity(great, good,semcor_ic))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1320, in lin_similarity
return synset1.lin_similarity(synset2, ic, verbose)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 789, in lin_similarity
ic1, ic2, lcs_ic = _lcs_ic(self, other, ic)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1645, in _lcs_ic
ic1 = information_content(synset1, ic)
File "/usr/local/lib/python2.7/dist-packages/nltk/corpus/reader/wordnet.py", line 1666, in information_content
raise WordNetError(msg % synset.pos)
nltk.corpus.reader.wordnet.WordNetError: Information content file has no entries for part-of-speech: s
>>> print max(wn.path_similarity(good,great), wn.path_similarity(great, good))None
None
Now seems like WordNet is creating more problems than it's solving anything here, let's try another means, let's try word clustering, see http://en.wikipedia.org/wiki/Word-sense_induction
This is when i also give up on answering the broad and opened question that the OP has posted because there's a LOT done in clustering that are automagics to mere mortals like me =)