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 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 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 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)
from wn import *
import re
w = wn("wordnet.db")
out = open("input.txt", 'w')
dets = ["the", "an", "a"]
all_sids = w.get_all_synsets()
for sid in all_sids:
tc = w.get_tagcount(sid)
if tc > 5:
gloss = w.get_gloss(sid).lower().strip()
lemma = w.get_lemma(sid).lower().strip()
words = gloss.split(" ")
first = words[0]
if first not in dets:
continue
sentence = "A %s is %s.\n" % (lemma, gloss)
out.write(sentence)
from wn import *
wn = wn('wordnet.db')
while True:
lemma = str(raw_input())
wid = wn.get_word_ids(lemma)
sids = wn.get_synset_ids(wid)
for sid in sids:
info = wn.synset_more_info(sid)
print info
print
from wn import *
wn = wn('wordnet.db')
while True:
lemma = str(raw_input())
wid = wn.get_word_ids(lemma)
sids = wn.get_synset_ids(wid)
for sid in sids:
info = wn.synset_more_info(sid)
print info
print
] #lematizare in engleza
print("Fraza lemmatizata engleza", lemmatized_sentence_en
) #nu am reusit sa il fac sa mearga si in romana
stemmer_en = snowballstemmer.stemmer('english')
stemmer_sentence_en = stemmer_en.stemWords(lemmatized_sentence_en)
print("Fraza dupa stemmer in en ", stemmer_sentence_en)
else: #teste:
#posibila alternativa lematizare in romana : https://github.com/dumitrescustefan/RoWordNet
wn = rwn.RoWordNet()
cuvant_initial = 'carte'
#stemmer_ro = snowballstemmer.stemmer('romanian');
# stemmer_sentence_ro = stemmer_ro.stemWords([cuvant_initial])
# print(stemmer_sentence_ro)
# synset_ids = wn.synsets(literal=stemmer_sentence_ro[0])
synset_ids = wn.synsets(literal=cuvant_initial)
if len(synset_ids) >= 1:
for synset_id in synset_ids:
print("Posibila lematizare pt ", cuvant_initial, ": literals=",
wn(synset_id).literals, " tip=",
wn(synset_id).pos)
else:
print("NU are lematizare in acest modul: ", cuvant_initial)
#wn.download('ronwn')
#w = wn.words('arbusti')[0]
#print(w.lemma())
#nltk.download()
#print("NLTK wordnet languages:", sorted(wn_nltk_test.langs()))
from wn import *
import re
w = wn("wordnet.db")
out = open("input.txt", 'w')
dets = ["the", "an", "a"]
all_sids = w.get_all_synsets()
for sid in all_sids:
tc = w.get_tagcount(sid)
if tc > 5:
gloss = w.get_gloss(sid).lower().strip()
lemma = w.get_lemma(sid).lower().strip()
words = gloss.split(" ")
first = words[0]
if first not in dets:
continue
sentence = "A %s is %s.\n" % (lemma, gloss)
def __init__(self):
self.wn = wn("wordnet.db")
nounToNoun = {}
nounToAdj = {}
nounToAdj = {
105611302: [300708498], # mind is brittle
107480068: [302091020], # emotion is blinding->concealing?
107541053: [300269989], # hope is light
107511733: [300269989], # (specific) hope is light
104846770: [300417413, 302314584], # morality is clean
104849241: [400096333, 300269989, 300393105], # good is up, white
105144079: [400095320, 300273082, 300392812], # bad is down, black
}
nounToNoun = {
107480068: [
302261386,
111458624,
105194578,
111466043,
100027167,
107309781,
201206218,
114395018,
], # emotions are liquids (in a person, in the eyes), forces
100007846: [102810471, 100015388, 100017222, 102913152, 103699975], # people are batteries
107516354: [111466043, 114686186], # anger is heat
201188485: [111466043], # lust is heat
107544647: [105725527], # affection is warmth
100759335: [114395018], # lust is madness
107543288: [114395018, 113742573, 100306426, 105967977], # love is madness
114379501: [202743020, 104738641], # emotional stability->sanity is balance
107541053: [100032613, 109918248, 111473954], # hope is a possession
107511733: [100032613, 109918248, 111473954], # (specific) hope is a possession
113928388: [100015388, 104194289, 104468005, 102958343], # relationship is an animal
104655442: [105085572], # emotional intimacy is physical closeness
113781820: [100148653], # emotional bonding is physical bonding
109622928: [100032613], # loved one is a possession
201776727: [301251128], # dislike is cold
107484265: [111458624, 105194578, 114039534], # desires are physical forces
107519253: [301251128], # fear is cold
104713118: [107027180], # emotional harmony is musical harmony
107503260: [114359952], # disgust is nausea
104887129: [100367280], # conceit is inflation
107508486: [113501548], # pride is swelling
100027807: [107309781, 103094503], # form is motion
109387222: [110151570], # paths are guides
104673965: [104151940], # appearance is a cover
101072402: [100019613], # laughter is a substance
100658082: [100973077], # treating illness is fighting a war
103740161: [104565375], # medicine is a weapon
114018567: [107334490], # intoxication is destruction
111473954: [114939900, 302261386], # light is a fluid
113983515: [115046900], # darkness is a solid
103699975: [100007846], # machines are people
113384557: [302261386], # money is a liquid
113333237: [103094503], # investments are containers
104846770: [104896161], # morality is cleanliness
106784003: [108630039, 109225146], # problems are regions
101129920: [100032613, 103679986, 103094503], # obligation is a possession
103094503: [105194578], # obligation is force
107966140: [105216365], # society is a body
}
# love is a journey
nounToNoun[107543288] = [100306426]
# career is a journey
nounToNoun[100282613] = [100306426]
# vehicle and person
nounToNoun[104524313] = [100007846]
# body and air
nounToNoun[108436288] = [108653314]
# harm is physical injury
nounToNoun[107420770] = [114285662]
# competition is a race
nounToNoun[101168569] = [107472657]
nounToNoun[long(105770926)] = [long(101170962)]
nounToNoun[long(105805475)] = [long(100803617), long(113465809), long(105710860)] # understanding
nounToNoun[long(105770926)] = [long(100243918), long(100278810)]
nounToNoun[long(106252138)] = [long(101057759)]
nounToNoun[long(100636921)] = [long(100310063)]
nounToNoun[long(105611302)] = [long(103094503), long(103699975)] # mind
nounToNoun[long(105833840)] = [
long(106362953),
long(109917593),
long(109918248),
long(105750657),
long(105154676),
] # idea
nounToNoun[long(105941423)] = [long(103051540), long(109622302), long(110151570), long(114070360)] # belief
nounToNoun[long(106283764)] = [long(104565375)]
self.NounToNoun = nounToNoun
self.NounToAdj = nounToAdj
def __init__(self):
self.wn = wn("wordnet.db")
nounToNoun = {}
nounToAdj = {}
nounToAdj = {
105611302: [300708498], # mind is brittle
107480068: [302091020], # emotion is blinding->concealing?
107541053: [300269989], # hope is light
107511733: [300269989], # (specific) hope is light
104846770: [300417413, 302314584], # morality is clean
104849241: [400096333, 300269989, 300393105], # good is up, white
105144079: [400095320, 300273082, 300392812], # bad is down, black
}
nounToNoun = {
107480068: [302261386, 111458624, 105194578, 111466043, 100027167, 107309781, 201206218, 114395018], # emotions are liquids (in a person, in the eyes), forces
100007846: [102810471, 100015388, 100017222, 102913152, 103699975], # people are batteries
107516354: [111466043, 114686186], # anger is heat
201188485: [111466043], # lust is heat
107544647: [105725527], # affection is warmth
100759335: [114395018], # lust is madness
107543288: [114395018, 113742573, 100306426, 105967977], # love is madness
114379501: [202743020, 104738641], # emotional stability->sanity is balance
107541053: [100032613, 109918248, 111473954], # hope is a possession
107511733: [100032613, 109918248, 111473954], # (specific) hope is a possession
113928388: [100015388, 104194289, 104468005, 102958343], # relationship is an animal
104655442: [105085572], # emotional intimacy is physical closeness
113781820: [100148653], # emotional bonding is physical bonding
109622928: [100032613], # loved one is a possession
201776727: [301251128], # dislike is cold
107484265: [111458624, 105194578, 114039534], # desires are physical forces
107519253: [301251128], # fear is cold
104713118: [107027180], # emotional harmony is musical harmony
107503260: [114359952], # disgust is nausea
104887129: [100367280], # conceit is inflation
107508486: [113501548], # pride is swelling
100027807: [107309781, 103094503], # form is motion
109387222: [110151570], # paths are guides
104673965: [104151940], # appearance is a cover
101072402: [100019613], # laughter is a substance
100658082: [100973077], # treating illness is fighting a war
103740161: [104565375], # medicine is a weapon
114018567: [107334490], # intoxication is destruction
111473954: [114939900, 302261386], # light is a fluid
113983515: [115046900], # darkness is a solid
103699975: [100007846], # machines are people
113384557: [302261386], # money is a liquid
113333237: [103094503], # investments are containers
104846770: [104896161], # morality is cleanliness
106784003: [108630039, 109225146], # problems are regions
101129920: [100032613, 103679986, 103094503], # obligation is a possession
103094503: [105194578], # obligation is force
107966140: [105216365], # society is a body
}
# love is a journey
nounToNoun[107543288] = [100306426]
# career is a journey
nounToNoun[100282613] = [100306426]
# vehicle and person
nounToNoun[104524313] = [100007846]
# body and air
nounToNoun[108436288] = [108653314]
# harm is physical injury
nounToNoun[107420770] = [114285662]
# competition is a race
nounToNoun[101168569] = [107472657]
nounToNoun[long(105770926)] = [long(101170962)]
nounToNoun[long(105805475)] = [long(100803617), long(113465809), long(105710860)] # understanding
nounToNoun[long(105770926)] = [long(100243918), long(100278810)]
nounToNoun[long(106252138)] = [long(101057759)]
nounToNoun[long(100636921)] = [long(100310063)]
nounToNoun[long(105611302)] = [long(103094503), long(103699975)] # mind
nounToNoun[long(105833840)] = [long(106362953), long(109917593), long(109918248), long(105750657), long(105154676)] #idea
nounToNoun[long(105941423)] = [long(103051540), long(109622302), long(110151570), long(114070360)] # belief
nounToNoun[long(106283764)] = [long(104565375)]
self.NounToNoun = nounToNoun
self.NounToAdj = nounToAdj
