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V3.py
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V3.py
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from wn import *
import berkeleyParser as parser
from wordreplace import *
import re
from MetaphorMapping import *
import fileinput
import operator
def get_stop_words():
stop_words = []
f = open("stop.txt",'r')
for word in fileinput.input("stop.txt"):
stop_words.append(word.strip())
return stop_words
# Remove stopwords from <words>:
def remove_stopwords(words, stopwords):
ret = []
for w in words:
if w not in stopwords:
ret.append(w)
return ret
# Jaccard similarity:
def jaccard(words1, words2):
shared = 0
total = len(words1) + len(words2)
for word in words1:
if word in words2:
#print "\t\t" + word
shared += 1
return float(shared) / float(total)
def SAP_distance(words1, words2, wordnet):
sum = 0
tots = len(words1) * len(words2)
for word1 in words1:
for word2 in words2:
sap = wordnet.SAP(word1, word2)
sum += sap
#print words1
#print words2
#print float(tots) / float(sum)
#print
#print
return float(tots) / float(sum)
def adj_test():
w = wn("wordnet.db")
adj = raw_input()
wid = w.get_word_ids(adj)
sids = w.get_synset_ids(wid)
for sid in sids:
print w.synset_info(sid)
rel = w.get_related_adjs(sid)
for r in rel:
print '\t'+w.synset_info(r)
def generate_noun_metaphors(sentence, parse, place):
wordnet = wn("wordnet.db")
stop_words = get_stop_words()
words = sentence.split(" ")
for i, word in enumerate(words):
words[i] = re.sub(r'\W+', '', word).lower()
noun = words[place[0]] # word to metaphorize
replace = words[place[1]] # word to replace
metmap = Mapping()
context = remove_stopwords(words, stop_words)
# Find metaphors ##########
possible = metmap.map(noun)
have_overlap = {}
overlap = {}
for sid in possible:
text = wordnet.get_text(sid)
text = remove_stopwords(text, stop_words)
sim = jaccard(text, context)
#print sim
if sim > 0.0:
have_overlap[sid] = text
overlap[sid] = sim
# Limit number of synsets by overlap
MAX = 5
keys = []
overlap = sorted(overlap.iteritems(), key=operator.itemgetter(1))
overlap = overlap[-1*MAX:]
for pair in overlap:
keys.append(pair[0])
max_similarity = -1.0
max_sid = None
for sid in keys:
text = have_overlap[sid]
# TODO - CAN USE JACCARD OR SAP ------------------------------------------------^^^^^^^
#sim = SAP_distance(text, context, wordnet)
sim = jaccard(text, context)
if sim > max_similarity:
max_sid = sid
max_similarity = sim
if max_sid is None:
return False, sentence
lemma = wordnet.get_lemma(max_sid)
#metaphor = wordnet.synset_info(max_sid)
#print "Found mapping from '%s' to: %s" % (word, metaphor)
return True, sentence.replace(replace, lemma)
def main():
input_file = "input.txt"
originals = []
for text in fileinput.input(input_file):
originals.append(text.strip())
output_file = "output.txt"
parser.parseFile(input_file, output_file)
parses = []
for parse in fileinput.input(output_file):
parses.append(parse.strip())
for i in range(len(parses)):
parse = parses[i]
original = originals[i]
try:
tree = parser.parseToTree(parse)
except:
print "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
continue
reps = findReplacements(tree, parse)
if "NN_NN" in reps.keys():
changed, result = generate_noun_metaphors(original, parse, reps["NN_NN"])
if changed:
print original
print result
print
print
if __name__ == "__main__":
main()