def test_not_in_wordnet(self):
self.assertEqual(not_in_wordnet("hot"), False)
# r = r.encode('ascii', 'ignore').decode('ascii') # bytes object is not being handled by most_similar function
try:
n = 0
sims = model.most_similar(positive=[r], topn=200)
for s in sims:
n = n + 1
print("counter: " + str(counter) + "-" + str(n))
# s = (s[0].encode("ascii", 'ignore'), s[1])
hit = False
search = True
if same_stem(s[0], r):
search = False
printout(",".join(['same stem', s[0], r, str(s[1]), str(n)]))
if not_in_wordnet(r):
search = False
printout(",".join(
['not_in_wordnet', s[0], r,
str(s[1]), str(n)]))
if search:
hyper = hypernomous(s[0], r)
hypo = hyoponomous(s[0], r)
syno = synononymous(s[0], r)
holo = holonymous(s[0], r)
mero = meronymous(s[0], r)
if hyper > 0 and hyper < 1:
printout(",".join(
['hyper', s[0], r,
str(s[1]),
str(n),
for r in words:
counter = counter + 1
r = r.encode('ascii', 'ignore')
try:
k = 0
sims = model.most_similar(positive=[r], topn=200)
for s in sims:
k = k + 1
for pos in parts_of_speech:
s = (s[0].encode("ascii", 'ignore'), s[1])
hit = False
search = True
if same_stem(s[0], r):
search = False
printout(",".join(['same stem', pos, str(model.similarity(s[0], r)), s[0], r, str(s[1]), str(counter) + "-" + str(k), str(1)]))
if not_in_wordnet(r, pos):
search = False
printout(",".join(['not_in_wordnet', pos, str(model.similarity(s[0], r)), s[0], r, str(s[1]), str(counter) + "-" + str(k), str(1)]))
if search:
hyper = hypernomous(r, s[0], pos)
hypo = hyoponomous(r, s[0], pos)
syno = synononymous(r, s[0], pos)
holo = holonymous(r, s[0], pos)
mero = meronymous(r, s[0], pos)
all_possible_hyper = get_all_possible_hypernyms(r, s[0], pos)
all_possible_hypo = get_all_possible_hyponyms(r, s[0], pos)
all_possible_mero = get_all_possible_meronyms(r, s[0], pos)
all_possible_holo = get_all_possible_holonyms(r, s[0], pos)
all_possible_syns = len(wn.synsets(r, pos=pos)) * len(wn.synsets(s[0], pos=pos))
tracker = str(counter) + "-" + str(k)
printout(",".join(['hyper', pos, str(model.similarity(s[0], r)), r.replace(',', ""), s[0].replace(',', ""), str(s[1]), tracker, str(hyper), str(all_possible_hyper)]))
for s in sims:
k = k + 1
for pos in parts_of_speech:
s = (s[0].encode("ascii", 'ignore'), s[1])
hit = False
search = True
if same_stem(s[0], r):
search = False
printout(",".join([
'same stem', pos,
str(model.similarity(s[0], r)), s[0], r,
str(s[1]),
str(counter) + "-" + str(k),
str(1)
]))
if not_in_wordnet(r, pos):
search = False
printout(",".join([
'not_in_wordnet', pos,
str(model.similarity(s[0], r)), s[0], r,
str(s[1]),
str(counter) + "-" + str(k),
str(1)
]))
if search:
hyper = hypernomous(r, s[0], pos)
hypo = hyoponomous(r, s[0], pos)
syno = synononymous(r, s[0], pos)
holo = holonymous(r, s[0], pos)
mero = meronymous(r, s[0], pos)
all_possible_hyper = get_all_possible_hypernyms(
for r in words:
counter = counter + 1
r = r.encode('ascii', 'ignore')
try:
n = 0
sims = model.most_similar(positive=[r], topn=200)
for s in sims:
n = n + 1
print "counter: " + str(counter) + "-" + str(n)
s = (s[0].encode("ascii", 'ignore'), s[1])
hit = False
search = True
if same_stem(s[0], r):
search = False
printout(",".join(['same stem', s[0], r, str(s[1]), str(n)]))
if not_in_wordnet(r):
search = False
printout(",".join(['not_in_wordnet', s[0], r, str(s[1]), str(n)]))
if search:
hyper = hypernomous(s[0], r)
hypo = hyoponomous(s[0], r)
syno = synononymous(s[0], r)
holo = holonymous(s[0], r)
mero = meronymous(s[0], r)
if hyper > 0 and hyper < 1:
printout(",".join(['hyper', s[0], r, str(s[1]), str(n), str(hyper)]))
if hypo > 0 and hypo < 1:
printout(",".join(['hypo', s[0], r, str(s[1]), str(n), str(hypo)]))
if syno > 0 and syno < 1:
printout(",".join(['syn', s[0], r, str(s[1]), str(n), str(syno)]))
if holo > 0 and holo < 1:
