def in_wordnet(word):
base = wordnet.morphy(word)
if base is None: base = word
for d in wordnet.Dictionaries.values():
if base in d: return True
if word in d: return True
return False
def in_wordnet(word):
base = wordnet.morphy(word)
if base is None:
base = word
for d in wordnet.Dictionaries.values():
if base in d:
return True
if word in d:
return True
return False
def select(cls, words):
keywords = []
for word in words:
woord = wn.morphy(word, wn.NOUN)
if (woord != None):
keywords.append(woord)
labels = []
labels.append(wn.synset('politics.n.01'))
labels.append(wn.synset('sport.n.01'))
labels.append(wn.synset('food.n.01'))
labels.append(wn.synset('party.n.01'))
labels.append(wn.synset('education.n.01'))
labels.append(wn.synset('book.n.01'))
labels.append(wn.synset('tv.n.01'))
labels.append(wn.synset('holiday.n.01'))
labels.append(wn.synset('computer.n.01'))
labels.append(wn.synset('science.n.01'))
names = [
'politics', 'sports', 'food', 'party', 'education', 'book', 'tv',
'holiday', 'computer', 'science'
]
#labels.append(wn.synset('animal.n.01'))
#names = ['politics', 'sports', 'food', 'party', 'education', 'book', 'tv', 'holiday', 'computer', 'science', 'animal']
length = len(labels)
interest = np.zeros(length)
for keyword in keywords:
keyword = keyword + '.n.01'
keyword = wn.synset(keyword)
for label in labels:
interest[labels.index(label)] = +keyword.path_similarity(label)
classification = []
#By changing n, one can output their top N interest classes
n = 3
for i in range(0, n):
element = max(xrange(len(interest)), key=lambda x: interest[x])
classification.append(names[element])
interest[element] = 0
return classification
def runWordnet():
#if False:
#for leaf in c.leaves():
#if(leaf[1]=="VBN" or leaf[1]=="VBD" or leaf[1]=="JJ"):
#if (re.match("TO|BE.*|DO.*|HV.*|MD|IN|PP", leaf[1])):
# cfdcohort[cohortvalue].inc(leaf)
# cfdleaves[leaf].inc(cohortvalue)
maxscore = -1
try:
verb = wordnet.morphy(leaf[0], wordnet.VERB)
senses = wordnet.V[verb]
for sense in senses:
ref_verbs = ["submit","obtain","enter","access","archive","retrieve","present","post","query","import","download","view","find","deposit"]
s = set([])
for ref in ref_verbs:
newscore = wordnet.V[ref][0].path_similarity(sense)
s.update(wordnet.V[ref][0].hypernym_paths()[0])
maxscore = max(maxscore,newscore)
print s,"\n\n"
except:
maxscore = -2
print maxscore
def stem(self, word):
return morphy(word)