def jaccard(sen_1, sen_2): tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(sen_1)) words = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ' or pos == '' or pos == 'VB' or pos == 'VBN' or pos == 'VBD' or pos == 'RB')] sen_set_1 = set(words) tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(sen_2)) words = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ' or pos == '' or pos == 'VB' or pos == 'VBN' or pos == 'VBD' or pos == 'RB')] sen_set_2 = set(words) jaccard_value = jaccard_distance(sen_set_1, sen_set_2) return jaccard_value
def similarity_by_jaccard(ox_defis, wn_defis):
matrix_similarity_jaccard = [[0 for x in range(len(ox_defis))] for x in range(len(wn_defis))];
for iWnWord in range(len(wn_defis)):
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wn_defis[iWnWord]));
words = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ' or pos == '' or pos == 'VB' or pos == 'VBN' or pos == 'VBD' or pos == 'RB')];
# words = nltk.wordpunct_tokenize(wn.synset(wn_defis[iWnWord].name()).definition());
# print words
for i in range(len(words)):
words[i] = wordnet_lemmatizer.lemmatize(words[i]);
wn_set = set(words);
# print "\n"
# print wn_set
# wn_set = set(wn.synset(wn_defis[iWnWord].name()).definition().split())
# print wn_set
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# word-word
for iDictWord in range(len(ox_defis)):
# if not ox_defis[str(iDictWord)].has_key("d") or dict_words[str(iDictWord)]["d"] == None:
# matrix_similarity_jaccard[iWnWord][iDictWord] = 1;
# continue
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(ox_defis[iDictWord]));
words = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ' or pos == '' or pos == 'VB' or pos == 'VBN' or pos == 'VBD' or pos == 'RB')];
# words = nltk.wordpunct_tokenize(ox_defis[str(iDictWord)]["d"]);
# print words
for i in range(len(words)):
words[i] = wordnet_lemmatizer.lemmatize(words[i]);
dict_set = set(words);
# print dict_set
# print
# dict_set = set(ox_defis[str(iDictWord)]["d"].encode('utf8').split());
matrix_similarity_jaccard[iWnWord][iDictWord] = 1 - jaccard_distance(wn_set,dict_set);
# matrix_similarity_jaccard[iWnWord][iDictWord] = cal_jacc_for_ngrams(wn_set, dict_set, 1)
########################################
return matrix_similarity_jaccard
def get_nouns(dict_words):
dict_words_nouns = []
for iWord in range(len(dict_words)):
print iWord
dict_words_nouns.append([])
wordDict = dict_words[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# sd
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["sd"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
print noun
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# d
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["d"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
########################################
return dict_words_nouns
def split_and_POS(sen):
# tokenize
tokens = nltk.wordpunct_tokenize(sen)
# pos tag
tagged_words = POSWrapper.pos_tag(tokens)
arr_pos = []
for (word, pos) in tagged_words:
arr_pos.append(pos)
return arr_pos
def preprocess_sentence_to_nouns(sentence): # tokenize tokens = nltk.wordpunct_tokenize(sentence) # pos tag tagged_words = POSWrapper.pos_tag(tokens) # get n nouns = [word for word, pos in tagged_words if check_pos_noun(pos)] # stemming # nouns_stemmed = [] # for noun in nouns: # noun_stemmed = __wordnet_lemmatizer__.lemmatize(noun, pos='n') # nouns_stemmed.append(noun_stemmed) # return nouns
def read_nouns():
dict_wn = {}
for synset in list(wn.all_synsets('n')):
key = synset.name() + "-" + synset.definition()
lemmas = [str(lemma.name()) for lemma in synset.lemmas()]
key += "="
for lemma in lemmas:
key = key + "-" + lemma
dict_wn[key] = lemmas
# # - - - - - - - - - - - - - - - - - - - - - - - - - - -
# # get hypernyms
# print "\nhypernyms ------";
for hypernym in synset.hypernyms():
for lemma in wn.synset(hypernym.name()).lemmas():
lemma_name = lemma.name();
dict_wn[key].append(lemma_name)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hyponyms
for hyponym in synset.hyponyms():
for lemma in wn.synset(hyponym.name()).lemmas():
lemma_name = lemma.name();
dict_wn[key].append(lemma_name)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get description
# print wn.synset(bank.name()).definition();
#
# for meronym in synset.part_meronyms():
# for lemma in wn.synset(meronym.name()).lemmas():
# lemma_name = lemma.name();
# dict_wn[key].append(lemma_name)
#
# for holonym in synset.member_holonyms():
# for lemma in wn.synset(holonym.name()).lemmas():
# lemma_name = lemma.name();
# dict_wn[key].append(lemma_name)
#
#
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(synset.definition()));
nouns = [word for word,pos in tagged_sent if pos == 'NN'];
for i in range(len(nouns)):
nouns[i] = wordnet_lemmatizer.lemmatize(nouns[i]);
for noun in nouns:
dict_wn[key].append(noun)
##
return dict_wn
def get_nbest_synsets_n_v_with_word_vn(dict_words,word_concept):
dict_words_nouns = [];
dict_synsets_nouns = [];
wn_words = wn.synsets(word_concept, pos = 'n');
if word_concept == 'bedroom':
asdf = 0;
for iWord in range(len(dict_words)):
print iWord;
dict_words_nouns.append([]);
dict_synsets_nouns.append([]);
wordDict = dict_words[iWord];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# sd
if not wordDict.has_key('tv'):
continue
if not wordDict.has_key('d'):
continue
nouns = [];
if wordDict.has_key("sd"):
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["sd"]));
nouns = [word for word,pos in tagged_sent if ((pos == 'NN' or pos == 'NNS') and (word != 'sth' and word != 'etc'))];
if len(nouns) == 0:
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
print tagged_sent
nouns = [word for word,pos in tagged_sent if ((pos == 'NN' or pos == 'NNS') and (word != 'sth' and word != 'etc'))];
elif wordDict.has_key("d") and wordDict["d"] != None:
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
print tagged_sent
nouns = [word for word,pos in tagged_sent if ((pos == 'NN' or pos == 'NNS') and (word != 'sth' or word != 'etc'))];
else:
continue
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
if noun != "sth" and noun != 'etc' and noun not in dict_words_nouns[iWord]:
dict_words_nouns[iWord].append(noun);
if len(dict_words_nouns[iWord]) == 0:
continue
print dict_words_nouns[iWord]
synsetsSD = [];
for word in dict_words_nouns[iWord]:
synsets = wn.synsets(word, pos = 'n');
for synset in synsets:
synsetsSD.append(synset)
if len(synsetsSD) == 0:
continue
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# d
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
nouns = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS')];
if wordDict.has_key('xh0') and wordDict['xh0'] is not None and wordDict['xh0'] != 'nn':
nouns.append(wordDict['xh0']);
# if wordDict.has_key('xh1') and wordDict['xh1'] is not None:
# nouns.append(wordDict['xh1']);
# if wordDict.has_key('xh2') and wordDict['xh2'] is not None:
# nouns.append(wordDict['xh2']);
# print tagged_sent
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
if noun.encode('utf8') != word_concept and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0;
pSD_max = 0;
for iSyn in range(len(synsetsSD)):
synsetSD = synsetsSD[iSyn];
pSD = 0;
arr_p = [];
for synset in wn_words:
p_noun_max = 0;
p = synsetSD.path_similarity(synset);
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
if p > p_noun_max:
p_noun_max = p;
arr_p.append(p_noun_max);
arr_p = sorted(arr_p, reverse=True);
for i in xrange(0, len(arr_p)-1):
if i <= 0:
pSD += arr_p[i];
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD;
# print iSyn
# print iSDMax
iSDMax = iSyn;
# print "\n"
synsetRoot = synsetsSD[iSDMax];
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos = 'n');
if len(synsets_noun) <= 0:
continue;
p_noun_max = 0;
synMax = synsets_noun[0];
for synset_noun in synsets_noun:
# dict_synsets_nouns[iWord].append(synMax);
for synset in wn_words:
p = synset.path_similarity(synset_noun);
# p = synsetRoot.path_similarity(synset_noun);
if p > p_noun_max:
p_noun_max = p;
synMax = synset_noun;
if synMax not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
# if len(synsetsSD) >= 1:
# synsetRoot = synsetsSD[0];
# print "synsetroot"
# print synsetRoot
#
# for noun in dict_words_nouns[iWord]:
# synsets_noun = wn.synsets(noun, pos = 'n');
# if len(synsets_noun) <= 0:
# continue;
#
# p_noun_max = 0;
# synMax = synsets_noun[0];
#
# for synset_noun in synsets_noun:
# p = synsetRoot.path_similarity(synset_noun);
# if p > p_noun_max:
# p_noun_max = p;
# synMax = synset_noun;
#
# if synMax not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
# if len(synsetsSD) >= 3:
# synsetRoot = synsetsSD[2];
# print "synsetroot"
# print synsetRoot
#
# for noun in dict_words_nouns[iWord]:
# synsets_noun = wn.synsets(noun, pos = 'n');
# if len(synsets_noun) <= 0:
# continue;
#
# p_noun_max = 0;
# synMax = synsets_noun[0];
#
# for synset_noun in synsets_noun:
# p = synsetRoot.path_similarity(synset_noun);
# if p > p_noun_max:
# p_noun_max = p;
# synMax = synset_noun;
#
# if synMax not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
# continue
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
nouns = [word for word,pos in tagged_sent if (pos == 'VB' or pos == 'VBN' or pos == 'VBD')];
print "VVVVV"
print nouns
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='v');
if noun == None:
continue
if noun.encode('utf8') != word_concept and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0;
pSD_max = 0;
for iSyn in range(len(synsetsSD)):
synsetSD = synsetsSD[iSyn];
pSD = 0;
arr_p = [];
for synset in wn_words:
p_noun_max = 0;
p = synsetSD.path_similarity(synset);
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
if p > p_noun_max:
p_noun_max = p;
arr_p.append(p_noun_max);
arr_p = sorted(arr_p, reverse=True);
for i in xrange(0, len(arr_p)-1):
if i <= 1:
pSD += arr_p[i];
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD;
# print iSyn
# print iSDMax
iSDMax = iSyn;
# print "\n"
synsetRoot = synsetsSD[iSDMax];
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos = 'v');
if len(synsets_noun) <= 0:
continue;
p_noun_max = 0;
synMax = synsets_noun[0];
for synset_noun in synsets_noun:
p = synsetRoot.path_similarity(synset_noun);
if p > p_noun_max:
p_noun_max = p;
synMax = synset_noun;
if synMax not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
print "dict_synsets_nouns"
print dict_synsets_nouns[iWord]
########################################
return dict_synsets_nouns;
def similarity_by_synsets_synsets_nbest_withword_average(WORD, dict_words):
if WORD == "bank":
asf = 0;
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# dictionary data
dict_words_synsets = get_nbest_synsets_n_v_with_word(dict_words,WORD);
# print "dict-word_synsets"
# print dict_words_synsets
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# wordnet data
wn_words = wn.synsets(WORD, pos = 'n');
print "wn_words -------"
print wn_words;
wn_words_synsets = WordnetProcess.get_synsets_n_v(WORD, wn_words);
print wn_words_synsets
# matrix for similarity dict_words vs wn_words
matrix_similarity = [[0 for x in range(len(dict_words))] for x in range(len(wn_words))];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
####################################################################################################
#
# calculate 2d matrix of p
for iWnWord in range(len(wn_words)):
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# word-word
for iDictWord in range(len(dict_words)):
p_iWnWord_iDictWord = 0.;
arr_p_word = [];
#
for dict_synset in dict_words_synsets[iDictWord]:
# print "------------ dict noun"
# print dictNoun;
p_dictNoun_wnNouns = 0;
# for some nouns don't have synsets
arr_p = [];
# - - - - - - - - - - - - - - - - - - - - - - - -
for wn_synset in wn_words_synsets[iWnWord]:
#
p_max = dict_synset.path_similarity(wn_synset);
if p_max == None:
continue
arr_p.append(p_max);
# print p_max
arr_p = sorted(arr_p, reverse=True);
nBest = 8;
count = 0.0001;
for i in xrange(0, len(arr_p)-1):
if i < nBest:
p_dictNoun_wnNouns += arr_p[i];
count += 1;
p_dictNoun_wnNouns = p_dictNoun_wnNouns/count;
arr_p_word.append(p_dictNoun_wnNouns);
arr_p_word = sorted(arr_p_word, reverse=True);
nBest = 10;
count = 5;
for i in range(len(arr_p_word)):
if i < nBest:
if nBest > len(arr_p_word):
if i == 0:
p_iWnWord_iDictWord += arr_p_word[i]*5.;
elif i< nBest/3:
p_iWnWord_iDictWord += arr_p_word[i]*1;
else:
p_iWnWord_iDictWord += arr_p_word[i]*1;
else:
if i == 0:
p_iWnWord_iDictWord += arr_p_word[i]*5.;
elif i< len(arr_p_word)/3:
p_iWnWord_iDictWord += arr_p_word[i]*1;
else:
p_iWnWord_iDictWord += arr_p_word[i]*1;
count += 1;
if count == 0:
p_iWnWord_iDictWord = 0;
else:
p_iWnWord_iDictWord = p_iWnWord_iDictWord/count
matrix_similarity[iWnWord][iDictWord] = p_iWnWord_iDictWord;
# - - - - - - - - - - - - - - - - - - - - - - - - -
# word-word
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
####################################################################################################
print "----------------------------------------------------"
s = [[str(e) for e in row] for row in matrix_similarity]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
print '\n'.join(table)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# dictionary data
wn_words = dict_words;
wn_words_synsets = get_nbest_synsets_n_v_with_word(wn_words,WORD);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# wordnet data
dict_words = wn.synsets(WORD, pos = 'n');
# print wn_words;
dict_words_synsets = WordnetProcess.get_synsets_n_v(WORD, dict_words);
print "sysnets -----------------------.----.-----.--.-"
# matrix for similarity dict_words vs wn_words
matrix_similarity_reverse = [[0 for x in range(len(dict_words))] for x in range(len(wn_words))];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
####################################################################################################
#
# calculate 2d matrix of p
for iWnWord in range(len(wn_words)):
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# word-word
for iDictWord in range(len(dict_words)):
p_iWnWord_iDictWord = 0.;
arr_p_word = [];
for dict_synset in dict_words_synsets[iDictWord]:
# print dictNoun;
p_dictNoun_wnNouns = 0;
# for some nouns don't have synsets
countwnNouns = 0.00000001;
arr_p = [];
# - - - - - - - - - - - - - - - - - - - - - - - -
for wn_synset in wn_words_synsets[iWnWord]:
p_max = dict_synset.path_similarity(wn_synset);
if p_max != None:
arr_p.append(p_max);
# print p_max
# - - - - - - - - - - - - - - - - - - - - - - - -
arr_p = sorted(arr_p, reverse=True);
nBest = 8;
count = 0.0001
for i in range(len(arr_p)):
if i < nBest:
p_dictNoun_wnNouns += arr_p[i];
count +=1
p_dictNoun_wnNouns = p_dictNoun_wnNouns/count;
arr_p_word.append(p_dictNoun_wnNouns);
arr_p_word = sorted(arr_p_word, reverse=True);
nBest = 10;
count = 5;
for i in xrange(0, len(arr_p_word)-1):
if i < nBest:
if nBest > len(arr_p_word):
if i == 0:
p_iWnWord_iDictWord += arr_p_word[i]*5;
elif i< nBest/3:
p_iWnWord_iDictWord += arr_p_word[i]*1.;
else:
p_iWnWord_iDictWord += arr_p_word[i]*1;
else:
if i == 0:
p_iWnWord_iDictWord += arr_p_word[i]*5.;
elif i< len(arr_p_word)/3:
p_iWnWord_iDictWord += arr_p_word[i]*1.;
else:
p_iWnWord_iDictWord += arr_p_word[i]*1;
count += 1;
if count == 0:
p_iWnWord_iDictWord = 0;
else:
p_iWnWord_iDictWord = p_iWnWord_iDictWord/count
matrix_similarity_reverse[iWnWord][iDictWord] = p_iWnWord_iDictWord;
# - - - - - - - - - - - - - - - - - - - - - - - - -
# word-word
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
####################################################################################################
print "----------------------------------------------------"
s = [[str(e) for e in row] for row in matrix_similarity_reverse]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
print '\n'.join(table)
# word-word
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
dict_words = wn_words;
wn_words = wn.synsets(WORD, pos = 'n');
for iWnWord in range(len(wn_words)):
for iDictWord in range(len(dict_words)):
matrix_similarity[iWnWord][iDictWord] = matrix_similarity[iWnWord][iDictWord] + matrix_similarity_reverse[iDictWord][iWnWord];
matrix_similarity[iWnWord][iDictWord] /= 2;
####################################################################################################
print "----------------------------------------------------"
s = [[str(e) for e in row] for row in matrix_similarity]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
print '\n'.join(table)
####################################################################################################
#
# @brief:
#
matrix_similarity_jaccard = [[0 for x in range(len(dict_words))] for x in range(len(wn_words))];
for iWnWord in range(len(wn_words)):
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wn.synset(wn_words[iWnWord].name()).definition()));
words = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ' or pos == '' or pos == 'VB' or pos == 'VBN' or pos == 'VBD' or pos == 'RB')];
# words = nltk.wordpunct_tokenize(wn.synset(wn_words[iWnWord].name()).definition());
# print words
for i in range(len(words)):
words[i] = wordnet_lemmatizer.lemmatize(words[i]);
wn_set = set(words);
# wn_set = set(wn.synset(wn_words[iWnWord].name()).definition().split())
# print wn_set
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# word-word
for iDictWord in range(len(dict_words)):
if not dict_words[str(iDictWord)].has_key("d") or dict_words[str(iDictWord)]["d"] == None:
matrix_similarity_jaccard[iWnWord][iDictWord] = 1;
continue
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(dict_words[str(iDictWord)]["d"]));
words = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ' or pos == '' or pos == 'VB' or pos == 'VBN' or pos == 'VBD' or pos == 'RB')];
# words = nltk.wordpunct_tokenize(dict_words[str(iDictWord)]["d"]);
# print words
for i in range(len(words)):
words[i] = wordnet_lemmatizer.lemmatize(words[i]);
dict_set = set(words);
# print
# dict_set = set(dict_words[str(iDictWord)]["d"].encode('utf8').split());
matrix_similarity_jaccard[iWnWord][iDictWord] = jaccard_distance(wn_set,dict_set);
for iWnWord in range(len(wn_words)):
for iDictWord in range(len(dict_words)):
matrix_similarity[iWnWord][iDictWord] = matrix_similarity[iWnWord][iDictWord]*10 + 2*(1-matrix_similarity_jaccard[iWnWord][iDictWord]);
matrix_similarity[iWnWord][iDictWord] /= 12;
####################################################################################################
print "----------------------------------------------------"
s = [[str(e) for e in row] for row in matrix_similarity]
lens = [max(map(len, col)) for col in zip(*s)]
fmt = '\t'.join('{{:{}}}'.format(x) for x in lens)
table = [fmt.format(*row) for row in s]
print '\n'.join(table)
####################################################################################################
#
# write file
# - - - - - - - - - - - - - - - - - - - - - - - - -
# col
arrColWn = [];
for i in range(len(wn_words)):
matrix_similarity[i].insert(0,wn.synset(wn_words[i].name()).definition());
# - - - - - - - - - - - - - - - - - - - - - - - - -
# row
arrRowDict = [];
arrRowDict.append("--");
for i in range(len(dict_words)):
if not dict_words[str(i)].has_key('tv'):
dict_words[str(i)]['tv'] = "--";
if dict_words[str(i)]['tv'] == None:
dict_words[str(i)]['tv'] = "--"
arrRowDict.append(dict_words[str(i)]["tv"].encode('utf8'));
FileProcess.write_to_excel_file("Results/"+WORD+"_synsets_synsets_nbest_withword_average.csv",arrRowDict,matrix_similarity)
def get_nouns(word_origin, wn_words):
wn_words_nouns = [];
for iWord in range(len(wn_words)):
print "- - - - - - - - - - - - - - - - - - - - - - - - - - -";
print iWord;
wn_words_nouns.append([]);
# get a bank in wn_words
wordDict = wn_words[iWord];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get synsets of bank
print "synsets -------";
synsets_of_word = wn.synset(wordDict.name());
for lemma in synsets_of_word.lemmas():
lemma_name = lemma.name();
if lemma_name != word_origin:
print lemma_name;
wn_words_nouns[iWord].append(lemma_name);
# # - - - - - - - - - - - - - - - - - - - - - - - - - - -
# # get hypernyms
# print "\nhypernyms ------";
# for hypernym in wn.synset(wordDict.name()).hypernyms():
#
# for lemma in wn.synset(hypernym.name()).lemmas():
# lemma_name = lemma.name();
# if lemma_name != "bank":
#
# if not(lemma_name in wn_words_nouns[iWord]):
# print lemma_name;
# wn_words_nouns[iWord].append(lemma_name);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hyponyms
print "\nhyponyms ------";
for hyponym in wn.synset(wordDict.name()).hyponyms():
for lemma in wn.synset(hyponym.name()).lemmas():
lemma_name = lemma.name();
if lemma_name != "bank":
if not(lemma_name in wn_words_nouns[iWord]):
print lemma_name;
wn_words_nouns[iWord].append(lemma_name);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get description
print "\ndefinition ------";
# print wn.synset(bank.name()).definition();
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wn.synset(wordDict.name()).definition()));
nouns = [word for word,pos in tagged_sent if pos == 'NN'];
for noun in nouns:
if noun != "bank" and noun != "sth" and noun not in wn_words_nouns[iWord]:
print noun;
wn_words_nouns[iWord].append(noun);
########################################
return wn_words_nouns
def get_synsets_n_v(word_origin, wn_words):
wn_words_synset = [];
for iWord in range(len(wn_words)):
print "- - - - - - - - - - - - - - - - - - - - - - - - - - -";
print iWord;
wn_words_synset.append([]);
# get a bank in wn_words
wordDict = wn_words[iWord];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get synsets of bank
synset_of_word = wn.synset(wordDict.name());
wn_words_synset[iWord].append(synset_of_word);
print synset_of_word
print "---"
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hypernyms
# for hypernym in wn.synset(wordDict.name()).hypernyms():
# print hypernym
# wn_words_synset[iWord].append(hypernym);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hyponyms
print "---"
for hyponym in wn.synset(wordDict.name()).part_meronyms():
print hyponym
wn_words_synset[iWord].append(hyponym);
# # - - - - - - - - - - - - - - - - - - - - - - - - - - -
# # get hyponyms
# print "---"
# for hyponym in wn.synset(wordDict.name()).member_holonyms():
# print hyponym
# wn_words_synset[iWord].append(hyponym);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hyponyms
print "---"
for hyponym in wn.synset(wordDict.name()).hyponyms():
print hyponym
wn_words_synset[iWord].append(hyponym);
# # - - - - - - - - - - - - - - - - - - - - - - - - - - -
# # get description
print "\ndefinition ------";
# print wn.synset(bank.name()).definition();
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wn.synset(wordDict.name()).definition()));
print tagged_sent
nouns = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ')];
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
# if noun != "bank" and noun != "sth" and noun not in wn_words_synset[iWord]:
synsetsDictNoun = wn.synsets(noun, pos = "n");
if len(synsetsDictNoun) > 0:
synsetMax = synsetsDictNoun[0];
p_max = 0;
for synsetNoun in synsetsDictNoun:
p = synsetNoun.path_similarity(synset_of_word);
if p > p_max:
p_max = p;
# # synsetMax = synsetNoun
print synsetMax
if synsetMax not in wn_words_synset[iWord]:
wn_words_synset[iWord].append(synsetMax);
# if synsetsDictNoun[0] not in wn_words_synset[iWord]:
# # wn_words_synset[iWord].append(synsetsDictNoun[0]);
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wn.synset(wordDict.name()).definition()));
nouns = [word for word,pos in tagged_sent if (pos == 'VB' or pos == 'VBD' or pos == 'VBN')];
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
# if noun != "bank" and noun != "sth" and noun not in wn_words_synset[iWord]:
synsetsDictNoun = wn.synsets(noun, pos = "v");
if len(synsetsDictNoun) > 0:
synsetMax = synsetsDictNoun[0];
p_max = 0;
for synsetNoun in synsetsDictNoun:
p = synsetNoun.path_similarity(synset_of_word);
if p > p_max:
p_max = p;
# # synsetMax = synsetNoun
print synsetMax
if synsetMax not in wn_words_synset[iWord]:
wn_words_synset[iWord].append(synsetMax);
# if synsetsDictNoun[0] not in wn_words_synset[iWord]:
# wn_words_synset[iWord].append(synsetsDictNoun[0]);
########################################
return wn_words_synset
def split_words(sen):
tokens = nltk.wordpunct_tokenize(sen)
tagged_words = POSWrapper.pos_tag(tokens)
return tagged_words
def get_synsets_for_word_in_wn(word_origin, wn_synsets_for_word_origin):
# arr synsets for arr words
# each word has an array of synsets
wn_synsets_for_words = [];
# add p
p_synsets_for_words = [];
for iWord in range(len(wn_synsets_for_word_origin)):
# print "- - - - - - - - - - - - - - - - - - - - - - - - - - -";
# print iWord;
wn_synsets_for_words.append([]);
# add p
p_synsets_for_words.append([]);
# get a bank in wn_words
wordDict = wn_synsets_for_word_origin[iWord];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get synsets of bank
synset_of_word = wn.synset(wordDict.name());
wn_synsets_for_words[iWord].append(synset_of_word);
# add p
p_synsets_for_words[iWord].append(1.5);
# print synset_of_word
# print "---"
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hypernyms
if PARAMETERS.DICT_WN_FEATURE_RELATION_hypernyms == 1:
# print "hypernyms"
for hypernym in wn.synset(wordDict.name()).hypernyms():
# print hypernym
wn_synsets_for_words[iWord].append(hypernym);
# add p
p_synsets_for_words[iWord].append(1.2);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get meronyms
if PARAMETERS.DICT_WN_FEATURE_RELATION_part_meronyms == 1:
# print "meronyms"
for meronym in wn.synset(wordDict.name()).part_meronyms():
# print meronym
wn_synsets_for_words[iWord].append(meronym);
# add p
p_synsets_for_words[iWord].append(1.2);
# # - - - - - - - - - - - - - - - - - - - - - - - - - - -
# # get holonyms
if PARAMETERS.DICT_WN_FEATURE_RELATION_member_holonyms == 1:
# print "holonyms"
for holonym in wn.synset(wordDict.name()).member_holonyms():
# print holonym
wn_synsets_for_words[iWord].append(holonym);
# add p
p_synsets_for_words[iWord].append(1.2);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# get hyponyms
if PARAMETERS.DICT_WN_FEATURE_RELATION_hyponyms == 1:
# print "hyponyms"
for hyponym in wn.synset(wordDict.name()).hyponyms():
# print hyponym
wn_synsets_for_words[iWord].append(hyponym);
# add p
p_synsets_for_words[iWord].append(1.2);
# # - - - - - - - - - - - - - - - - - - - - - - - - - - -
# # get description
if PARAMETERS.DICT_WN_FEATURE_RELATION_definition == 1:
# print "\ndefinition ------";
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wn.synset(wordDict.name()).definition()));
# print tagged_sent
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
if PARAMETERS.POS_FEATURE_n == 1:
nouns = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS' or pos == 'JJ')];
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
if noun != word_origin and noun != "sth":
synsetsDictNoun = WordnetHandler.get_synsets_for_word(noun, "n");
if len(synsetsDictNoun) > 0:
synsetMax = synsetsDictNoun[0];
p_max = 0;
for synsetNoun in synsetsDictNoun:
p = synsetNoun.path_similarity(synset_of_word);
if p > p_max:
p_max = p;
synsetMax = synsetNoun
# print synsetMax
if synsetMax not in wn_synsets_for_words[iWord]:
wn_synsets_for_words[iWord].append(synsetMax);
# add p
p_synsets_for_words[iWord].append(1.);
# if synsetsDictNoun[0] not in wn_words_synset[iWord]:
# # wn_words_synset[iWord].append(synsetsDictNoun[0]);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
if PARAMETERS.POS_FEATURE_v == 1:
verbs = [word for word,pos in tagged_sent if (pos == 'VB' or pos == 'VBD' or pos == 'VBN')];
for verb in verbs:
verb = wordnet_lemmatizer.lemmatize(verb, pos='v');
if verb == None:
continue
if verb != "bank":
synsetsDictVerb = WordnetHandler.get_synsets_for_word(verb, "v");
if len(synsetsDictVerb) > 0:
synsetMax = synsetsDictVerb[0];
p_max = 0;
for synsetVerb in synsetsDictVerb:
p = synsetVerb.path_similarity(synset_of_word);
if p > p_max:
p_max = p;
synsetMax = synsetVerb
#
# print synsetMax
if synsetMax not in wn_synsets_for_words[iWord]:
wn_synsets_for_words[iWord].append(synsetMax);
# add p
p_synsets_for_words[iWord].append(1.);
# if synsetsDictNoun[0] not in wn_words_synset[iWord]:
# wn_words_synset[iWord].append(synsetsDictNoun[0]);
# print wn_synsets_for_words[iWord]
########################################
return wn_synsets_for_words,p_synsets_for_words;
def get_nbest_synsets_for_word_in_oxford(dict_words,word_concept):
dict_words_nouns = [];
dict_words_verbs = [];
dict_synsets_for_words = [];
wn_words = WordnetHandler.get_synsets_for_word(word_concept, 'n');
# add p
p_synsets_for_words = [];
for iWord in range(len(dict_words)):
# print iWord;
dict_words_nouns.append([]);
dict_words_verbs.append([]);
dict_synsets_for_words.append([]);
# add p
p_synsets_for_words.append([]);
wordDict = dict_words[str(iWord)];
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# sd
if not wordDict.has_key('tv'):
continue
if not wordDict.has_key('d'):
continue
nouns = [];
if wordDict.has_key("sd") and PARAMETERS.DICT_OX_FEATURE_RELATION_sd == 1:
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["sd"]));
nouns = [word for word,pos in tagged_sent if ((pos == 'NN' or pos == 'NNS') and (word != 'sth' and word != 'etc'))];
if len(nouns) == 0:
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
# print tagged_sent
nouns = [word for word,pos in tagged_sent if ((pos == 'NN' or pos == 'NNS') and (word != 'sth' and word != 'etc'))];
elif wordDict.has_key("d") and wordDict["d"] != None:
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
# print tagged_sent
nouns = [word for word,pos in tagged_sent if ((pos == 'NN' or pos == 'NNS') and (word != 'sth' or word != 'etc'))];
else:
continue
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
if noun != "sth" and noun != 'etc' and noun not in dict_words_nouns[iWord]:
dict_words_nouns[iWord].append(noun);
if len(dict_words_nouns[iWord]) == 0:
continue
# print dict_words_nouns[iWord]
synsetsSD = [];
for word in dict_words_nouns[iWord]:
synsets = WordnetHandler.get_synsets_for_word(word, 'n');
for synset in synsets:
synsetsSD.append(synset)
if len(synsetsSD) == 0:
continue
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# d
if PARAMETERS.DICT_OX_FEATURE_RELATION_d == 1:
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
nouns = [word for word,pos in tagged_sent if (pos == 'NN' or pos == 'NNS')];
if PARAMETERS.DICT_OX_FEATURE_RELATION_xh == 1:
if wordDict.has_key('xh0') and wordDict['xh0'] is not None and wordDict['xh0'] != 'nn':
nouns.append(wordDict['xh0']);
if wordDict.has_key('xh1') and wordDict['xh1'] is not None:
nouns.append(wordDict['xh1']);
if wordDict.has_key('xh2') and wordDict['xh2'] is not None:
nouns.append(wordDict['xh2']);
# print tagged_sent
for noun in nouns:
noun = wordnet_lemmatizer.lemmatize(noun, pos='n');
if noun == None:
continue
if noun.encode('utf8') != word_concept and noun != "sth" and noun not in dict_words_nouns[iWord]:
dict_words_nouns[iWord].append(noun);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# print wordDict["tv"]
# print dict_words_nouns[iWord]
#
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0;
pSD_max = 0;
for iSyn in range(len(synsetsSD)):
synsetSD = synsetsSD[iSyn];
pSD = 0;
arr_p = [];
for synset in wn_words:
# p_noun_max = 0;
p = synsetSD.path_similarity(synset);
# print "-----------------------"
# if p > p_noun_max:
p_noun_max = p;
arr_p.append(p_noun_max);
arr_p = sorted(arr_p, reverse=True);
for i in xrange(0, len(arr_p)-1):
if i <= 0:
pSD += arr_p[i];
# print "\n"
if pSD > pSD_max:
pSD_max = pSD;
iSDMax = iSyn;
# print "\n"
synsetRoot = synsetsSD[iSDMax];
# print "synsetroot"
# print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = WordnetHandler.get_synsets_for_word(noun, 'n');
if len(synsets_noun) <= 0:
continue;
p_noun_max = 0;
synMax = synsets_noun[0];
for synset_noun in synsets_noun:
# dict_synsets_nouns[iWord].append(synMax);
for synset in wn_words:
p = synset.path_similarity(synset_noun);
# p = synsetRoot.path_similarity(synset_noun);
if p > p_noun_max:
p_noun_max = p;
synMax = synset_noun;
if synMax not in dict_synsets_for_words[iWord]:
dict_synsets_for_words[iWord].append(synMax);
if PARAMETERS.POS_FEATURE_v:
# continue
tagged_sent = POSWrapper.pos_tag(nltk.wordpunct_tokenize(wordDict["d"]));
verbs = [word for word,pos in tagged_sent if (pos == 'VB' or pos == 'VBN' or pos == 'VBD')];
# print "VVVVV"
# print verbs
for verb in verbs:
verb = wordnet_lemmatizer.lemmatize(verb, pos='v');
if verb == None:
continue
if verb.encode('utf8') != word_concept and verb != "sth" and verb not in dict_words_verbs[iWord]:
# print noun;
dict_words_verbs[iWord].append(verb);
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
# print dict_words_verbs[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0;
pSD_max = 0;
for iSyn in range(len(synsetsSD)):
synsetSD = synsetsSD[iSyn];
pSD = 0;
arr_p = [];
for synset in wn_words:
# p_noun_max = 0;
p = synsetSD.path_similarity(synset);
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
# if p > p_noun_max:
p_verb_max = p;
arr_p.append(p_verb_max);
arr_p = sorted(arr_p, reverse=True);
for i in xrange(0, len(arr_p)-1):
if i <= 1:
pSD += arr_p[i];
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD;
# print iSyn
# print iSDMax
iSDMax = iSyn;
# print "\n"
synsetRoot = synsetsSD[iSDMax];
# print "synsetroot"
# print synsetRoot
for verb in dict_words_verbs[iWord]:
synsets_verb = WordnetHandler.get_synsets_for_word(verb, 'v');
if len(synsets_verb) <= 0:
continue;
p_verb_max = 0;
synMax = synsets_verb[0];
for synset_verb in synsets_verb:
# p = synsetRoot.path_similarity(synset_verb);
for synset in wn_words:
p = synset.path_similarity(synset_verb);
if p > p_verb_max:
p_verb_max = p;
synMax = synset_verb;
if synMax not in dict_synsets_for_words[iWord]:
dict_synsets_for_words[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
# print "dict_synsets_nouns"
# print dict_synsets_for_words[iWord]
########################################
return dict_synsets_for_words;
def get_synsets(dict_words):
dict_words_nouns = []
dict_synsets_nouns = []
for iWord in range(len(dict_words)):
print iWord
dict_words_nouns.append([])
dict_synsets_nouns.append([])
wordDict = dict_words[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# sd
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["sd"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
if noun != "sth" and noun != "etc" and noun not in dict_words_nouns[iWord]:
dict_words_nouns[iWord].append(noun)
print dict_words_nouns[iWord]
synsetsSD = wn.synsets(dict_words_nouns[iWord][len(dict_words_nouns[iWord]) - 1], pos="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# d
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["d"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0
pSD_max = 0
for iSyn in range(len(synsetsSD) - 1):
synsetSD = synsetsSD[iSyn]
pSD = 0
for iNoun in range(len(dict_words_nouns[iWord]) - 1):
if iNoun == 0:
continue
synsets_noun = wn.synsets(dict_words_nouns[iWord][iNoun], pos="n")
p_noun_max = 0
for synset_noun in synsets_noun:
p = synsetSD.path_similarity(synset_noun)
# print synsetSD
# print synset_noun
# print p
if p > p_noun_max:
p_noun_max = p
pSD += p_noun_max
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD
# print iSyn
# print iSDMax
iSDMax = iSyn
# print "\n"
synsetRoot = synsetsSD[0]
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos="n")
if len(synsets_noun) <= 0:
continue
p_noun_max = 0
synMax = synsets_noun[0]
for synset_noun in synsets_noun:
p = synsetRoot.path_similarity(synset_noun)
if p > p_noun_max:
p_noun_max = p
synMax = synset_noun
dict_synsets_nouns[iWord].append(synMax)
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
print "dict_synsets_nouns"
print dict_synsets_nouns[iWord]
########################################
return dict_synsets_nouns
def get_nbest_synsets_n_v_x_with_word(dict_words, word_concept):
dict_words_nouns = []
dict_synsets_nouns = []
wn_words = wn.synsets(word_concept, pos="n")
for iWord in range(len(dict_words)):
print iWord
dict_words_nouns.append([])
dict_synsets_nouns.append([])
wordDict = dict_words[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# sd
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["sd"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
noun = wn.morphy(noun)
if noun == None:
continue
if noun != "sth" and noun != "etc" and noun not in dict_words_nouns[iWord]:
dict_words_nouns[iWord].append(noun)
print dict_words_nouns[iWord]
synsetsSD = wn.synsets(dict_words_nouns[iWord][len(dict_words_nouns[iWord]) - 1], pos="n")
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# d
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["d"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
noun = wn.morphy(noun)
if noun == None:
continue
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0
pSD_max = 0
for iSyn in range(len(synsetsSD) - 1):
synsetSD = synsetsSD[iSyn]
pSD = 0
arr_p = []
for synset in wn_words:
p_noun_max = 0
p = synsetSD.path_similarity(synset)
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
if p > p_noun_max:
p_noun_max = p
arr_p.append(p_noun_max)
arr_p = sorted(arr_p, reverse=True)
for i in xrange(0, len(arr_p) - 1):
if i <= 1:
pSD += arr_p[i]
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD
# print iSyn
# print iSDMax
iSDMax = iSyn
# print "\n"
synsetRoot = synsetsSD[iSDMax]
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos="n")
if len(synsets_noun) <= 0:
continue
p_noun_max = 0
synMax = synsets_noun[0]
for synset_noun in synsets_noun:
p = synsetRoot.path_similarity(synset_noun)
if p > p_noun_max:
p_noun_max = p
synMax = synset_noun
if synMax not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synMax)
if synsets_noun[0] not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synsets_noun[0])
# if len(synsetsSD) >= 1:
# synsetRoot = synsetsSD[0];
# print "synsetroot"
# print synsetRoot
#
# for noun in dict_words_nouns[iWord]:
# synsets_noun = wn.synsets(noun, pos = 'n');
# if len(synsets_noun) <= 0:
# continue;
#
# p_noun_max = 0;
# synMax = synsets_noun[0];
#
# for synset_noun in synsets_noun:
# p = synsetRoot.path_similarity(synset_noun);
# if p > p_noun_max:
# p_noun_max = p;
# synMax = synset_noun;
#
# if synMax not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
# if len(synsetsSD) >= 3:
# synsetRoot = synsetsSD[2];
# print "synsetroot"
# print synsetRoot
#
# for noun in dict_words_nouns[iWord]:
# synsets_noun = wn.synsets(noun, pos = 'n');
# if len(synsets_noun) <= 0:
# continue;
#
# p_noun_max = 0;
# synMax = synsets_noun[0];
#
# for synset_noun in synsets_noun:
# p = synsetRoot.path_similarity(synset_noun);
# if p > p_noun_max:
# p_noun_max = p;
# synMax = synset_noun;
#
# if synMax not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["d"]))
nouns = [word for word, pos in tagged_sent if (pos == "VB" or pos == "VBN" or pos == "VBD")]
print "VVVVV"
print nouns
for noun in nouns:
noun = wn.morphy(noun)
if noun == None:
continue
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0
pSD_max = 0
for iSyn in range(len(synsetsSD) - 1):
synsetSD = synsetsSD[iSyn]
pSD = 0
arr_p = []
for synset in wn_words:
p_noun_max = 0
p = synsetSD.path_similarity(synset)
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
if p > p_noun_max:
p_noun_max = p
arr_p.append(p_noun_max)
arr_p = sorted(arr_p, reverse=True)
for i in xrange(0, len(arr_p) - 1):
if i <= 1:
pSD += arr_p[i]
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD
# print iSyn
# print iSDMax
iSDMax = iSyn
# print "\n"
synsetRoot = synsetsSD[iSDMax]
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos="v")
if len(synsets_noun) <= 0:
continue
p_noun_max = 0
synMax = synsets_noun[0]
for synset_noun in synsets_noun:
p = synsetRoot.path_similarity(synset_noun)
if p > p_noun_max:
p_noun_max = p
synMax = synset_noun
if synMax not in dict_synsets_nouns[iWord]:
print synMax
dict_synsets_nouns[iWord].append(synMax)
if synsets_noun[0] not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synsets_noun[0])
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# x1
if wordDict.has_key("x1"):
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["x1"]))
nouns = [word for word, pos in tagged_sent if (pos == "NN" or pos == "NNS")]
for noun in nouns:
noun = wn.morphy(noun)
if noun == None:
continue
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0
pSD_max = 0
for iSyn in range(len(synsetsSD) - 1):
synsetSD = synsetsSD[iSyn]
pSD = 0
arr_p = []
for synset in wn_words:
p_noun_max = 0
p = synsetSD.path_similarity(synset)
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
if p > p_noun_max:
p_noun_max = p
arr_p.append(p_noun_max)
arr_p = sorted(arr_p, reverse=True)
for i in xrange(0, len(arr_p) - 1):
if i <= 1:
pSD += arr_p[i]
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD
# print iSyn
# print iSDMax
iSDMax = iSyn
# print "\n"
synsetRoot = synsetsSD[iSDMax]
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos="n")
if len(synsets_noun) <= 0:
continue
p_noun_max = 0
synMax = synsets_noun[0]
for synset_noun in synsets_noun:
p = synsetRoot.path_similarity(synset_noun)
if p > p_noun_max:
p_noun_max = p
synMax = synset_noun
if synMax not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synMax)
if synsets_noun[0] not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synsets_noun[0])
# if len(synsetsSD) >= 1:
# synsetRoot = synsetsSD[0];
# print "synsetroot"
# print synsetRoot
#
# for noun in dict_words_nouns[iWord]:
# synsets_noun = wn.synsets(noun, pos = 'n');
# if len(synsets_noun) <= 0:
# continue;
#
# p_noun_max = 0;
# synMax = synsets_noun[0];
#
# for synset_noun in synsets_noun:
# p = synsetRoot.path_similarity(synset_noun);
# if p > p_noun_max:
# p_noun_max = p;
# synMax = synset_noun;
#
# if synMax not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
# if len(synsetsSD) >= 3:
# synsetRoot = synsetsSD[2];
# print "synsetroot"
# print synsetRoot
#
# for noun in dict_words_nouns[iWord]:
# synsets_noun = wn.synsets(noun, pos = 'n');
# if len(synsets_noun) <= 0:
# continue;
#
# p_noun_max = 0;
# synMax = synsets_noun[0];
#
# for synset_noun in synsets_noun:
# p = synsetRoot.path_similarity(synset_noun);
# if p > p_noun_max:
# p_noun_max = p;
# synMax = synset_noun;
#
# if synMax not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synMax);
# if synsets_noun[0] not in dict_synsets_nouns[iWord]:
# dict_synsets_nouns[iWord].append(synsets_noun[0]);
tagged_sent = POSWrapper.pos_tag(nltk.word_tokenize(wordDict["x1"]))
nouns = [word for word, pos in tagged_sent if (pos == "VB" or pos == "VBN" or pos == "VBD")]
print "VVVVV"
print nouns
for noun in nouns:
noun = wn.morphy(noun)
if noun == None:
continue
if noun != wordDict["en"] and noun != "sth" and noun not in dict_words_nouns[iWord]:
# print noun;
dict_words_nouns[iWord].append(noun)
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
print wordDict["tv"]
print dict_words_nouns[iWord]
# - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# synsets
iSDMax = 0
pSD_max = 0
for iSyn in range(len(synsetsSD) - 1):
synsetSD = synsetsSD[iSyn]
pSD = 0
arr_p = []
for synset in wn_words:
p_noun_max = 0
p = synsetSD.path_similarity(synset)
# arr_p.append(p);
# print "-----------------------"
# print synsetSD
# print synset
# print p
if p > p_noun_max:
p_noun_max = p
arr_p.append(p_noun_max)
arr_p = sorted(arr_p, reverse=True)
for i in xrange(0, len(arr_p) - 1):
if i <= 1:
pSD += arr_p[i]
# print "\n"
if pSD > pSD_max:
# print pSD
# print pSD_max
pSD_max = pSD
# print iSyn
# print iSDMax
iSDMax = iSyn
# print "\n"
synsetRoot = synsetsSD[iSDMax]
print "synsetroot"
print synsetRoot
for noun in dict_words_nouns[iWord]:
synsets_noun = wn.synsets(noun, pos="v")
if len(synsets_noun) <= 0:
continue
p_noun_max = 0
synMax = synsets_noun[0]
for synset_noun in synsets_noun:
p = synsetRoot.path_similarity(synset_noun)
if p > p_noun_max:
p_noun_max = p
synMax = synset_noun
if synMax not in dict_synsets_nouns[iWord]:
print synMax
dict_synsets_nouns[iWord].append(synMax)
if synsets_noun[0] not in dict_synsets_nouns[iWord]:
dict_synsets_nouns[iWord].append(synsets_noun[0])
print "dict_synsets_nouns"
print dict_synsets_nouns[iWord]
########################################
return dict_synsets_nouns
