def main():
db = PwdDb()
tg = TagsetConverter() # assumes the pwds are pos-tagged using Brown tags
offset = 0
size = 1000000
#output_file = open('../results/semantic/nouns/{0}_{1}.txt'.format(offset, size), 'wb')
output_file = open('../results/pos/verbs/all.txt'.format(offset, size), 'wb')
while (db.hasNext()):
# for i in range(offset,offset+size):
words = db.nextPwd() # list of Words
for w in words:
if w.pos is None :
continue
wn_pos = tg.brownToWordNet(w.pos)
if wn_pos == 'v':
output_file.write(str(w.word) + '\n')
db.finish()
return 0
def main():
db = PwdDb()
tc = TagsetConverter() # assumes the pwds are pos-tagged using Brown tags
pos_dist = dict()
wn_pos_dist = dict()
fragments_total = 0 # number of words
pos_total = 0 # number of pos-tagged words
wn_verbs_total = 0 # number of verbs that are found in wordnet
wn_nouns_total = 0 # number of verbs that are found in wordnet
while (db.hasNext()):
words = db.nextPwd() # list of Words
fragments_total += len(words)
for w in words:
if w.pos is None :
continue
pos_total += 1
if w.pos in pos_dist :
pos_dist[w.pos] += 1
else :
pos_dist[w.pos] = 1
wn_pos = tc.brownToWordNet(w.pos)
if wn_pos in wn_pos_dist :
wn_pos_dist[wn_pos] += 1
else :
wn_pos_dist[wn_pos] = 1
if w.synsets is not None:
if wn_pos == 'v' :
wn_verbs_total += 1
elif wn_pos == 'n' :
wn_nouns_total += 1
db.finish()
# convert to list of tuples so we can sort it by value
pos_dist = pos_dist.items()
pos_dist = sorted(pos_dist, key = lambda entry: entry[1], reverse=True)
print "Total number of fragments: {}".format(fragments_total)
print 'of which {} are POS tagged words ({}%)'.format(pos_total, float(pos_total)*100/fragments_total)
print '\nPOS distribution (Brown tagset):\n'
for k, v in pos_dist:
print "{}\t{}".format(k, v)
print '\nPOS distribution (WordNet tagset):\n', wn_pos_dist
print '\n{} verbs found in WordNet ({}% of verbs)'.format(wn_verbs_total, float(wn_verbs_total)*100/wn_pos_dist['v'])
print '\n{} nouns found in WordNet ({}% of nouns)'.format(wn_nouns_total, float(wn_nouns_total)*100/wn_pos_dist['n'])
return 0
def main():
""" Tags the passwords by semantic categories,
assuming it's already pos and sentiment-tagged.
It doesn't need the sentiment info, but it gets the
synset # that was gotten in the sentiment tagging
process, to reduce overhead. """
db = PwdDb()
tagger = SemanticTagger()
tg = TagsetConverter() # assumes the pwds are pos-tagged using Brown tags
print "tagging process initialized..."
start = time()
csv_writer = csv.writer(open("../results/semantic/test.csv","wb"), dialect='excel')
# while (db.hasNext()):
for i in range(1,100001):
words = db.nextPwd() # list of Words
for w in words:
t = None
# if there's a synset for this word
if w.synsets is not None:
wn_pos = tg.brownToWordNet(w.pos)
t = tagger.tag(w.word, wn_pos, w.synsets)
else:
t = tagger.tag(w.word, w.pos)
w.category = t
db.saveCategory(w)
csv_writer.writerow([i, w.word, w.category, w.senti, w.pos])
db.finish()
print "tagging process took " + str(time()-start) + " seconds."
return 0;
in wordnet. In addition I wanted to see the impact on the pos-tagging of the
passwords and review my mapping brown -> wordnet.
Created on 2013-03-12
@author: rafa
'''
from database import PwdDb
from tagset_conversion import TagsetConverter
if __name__ == '__main__':
db = PwdDb()
tagconverter = TagsetConverter()
# tags not convered by wordnet
notcovered = dict()
while db.hasNext() :
p = db.nextPwd()
for w in p :
if w.pos is not None and tagconverter.brownToWordNet(w.pos) is None :
freq = notcovered[w.pos] if w.pos in notcovered else 0
notcovered[w.pos] = freq + 1
db.finish()
print notcovered