def synsets_for_word(word_pos):
(word, pos) = word_pos
synsets_of_noun = []
if pos_is_noun(pos):
synsets_of_noun = WordnetHandler.get_synsets_for_word(word, "n")
if pos_is_verb(pos):
synsets_of_noun = WordnetHandler.get_synsets_for_word(word, "v")
return synsets_of_noun
def sim_ox_wn_value_main_synsets(word):
dict_vectors_wn = WordnetParseDefinition.get_dict_vectors_value_for(word)
synsets_wn = WordnetHandler.get_synsets_for_word(word,'n')
dict_vectors_ox = OxParseDefinition.get_vectors_value_for_word(word, synsets_wn)
(keys_wn, vectors_wn) = Util.get_keys_values_of_dict(dict_vectors_wn)
(keys_ox, vectors_ox) = Util.get_keys_values_of_dict(dict_vectors_ox)
m2d_sim_defi_temp = sim_ox_wn_defi_WDS_via_main_syns(word)
DebugHandler.print_2d_matrix(m2d_sim_defi_temp)
m2d_sim_defi = [[0 for x in range(len(vectors_wn))] for x in range(len(vectors_ox))]
for i in range(len(vectors_wn)):
for j in range(len(vectors_ox)):
m2d_sim_defi[j][i] = m2d_sim_defi_temp[i][j]
m2d_sim = [[0 for x in range(len(vectors_ox))] for x in range(len(vectors_wn))]
for i in range(len(vectors_wn)):
vector_wn = vectors_wn[i]
print vector_wn
for j in range(len(vectors_ox)):
vector_ox = vectors_ox[j]
cosine = spatial.distance.cosine(m2d_sim_defi[j], vector_wn)
m2d_sim[i][j] = cosine
print "\n"
for j in range(len(vectors_ox)):
vector_ox = vectors_ox[j]
print vector_ox
return m2d_sim
def get_greatest_synsets_similarity_between(synsets_wn, nouns):
synset_wn_max = None
p_max = 0
if len(nouns) != 0:
for synset_wn in synsets_wn:
p_noun = 0
for noun in nouns:
synsets_of_noun = WordnetHandler.get_synsets_for_word(noun, 'n')
if len(synsets_of_noun) > 0:
p_each_noun = 0
for synset_of_noun in synsets_of_noun:
# p = synset_wn.path_similarity(synset_of_noun)
p = WordnetHandler.cal_similarity(synset_wn, synset_of_noun)
p_each_noun += p
p_each_noun = p_each_noun/len(synsets_of_noun)
p_noun += p_each_noun
p = p_noun/len(nouns)
if p > p_max:
synset_wn_max = synset_wn
else:
print "no nouns"
return synset_wn_max
def sim_ox_wn_defi_WDS_via_main_syns(word):
dict_vectors_wn = WordnetParseDefinition.get_dict_vectors_synsets_for_word(word)
synsets_wn = WordnetHandler.get_synsets_for_word(word,'n')
dict_vectors_ox = OxParseDefinition.get_dict_vectors_synsets_for_word(word, synsets_wn)
#
(keys_wn, vectors_wn) = Util.get_keys_values_of_dict(dict_vectors_wn)
(keys_ox, vectors_ox) = Util.get_keys_values_of_dict(dict_vectors_ox)
#
m2d_sim = sim_wn_ox_vector(vectors_ox, vectors_wn)
#
cal_sim_ngrams(word)
# write to file
# # - - - - - - - - - - - - - - - - - - - - - - - - -
# for i in range(len(keys_wn)):
# m2d_sim[i].insert(0,keys_wn[i]);
# # - - - - - - - - - - - - - - - - - - - - - - - - -
# # row
# row_dict = [];
# row_dict.append(word);
# for i in range(len(keys_ox)):
# row_dict.append(keys_ox[i].encode('utf8'));
# # - - - - - - - - - - - - - - - - - - - - - - - - -
# filename = 'Results/vector_definition/' + word + '.csv'
# FileProcess.append_to_excel_file(filename, row_dict, m2d_sim)
# # - - - - - - - - - - - - - - - - - - - - - - - - -
return m2d_sim
def get_dict_vectors_value_for(word):
vectors = OrderedDict()
synsets = WordnetHandler.get_synsets_for_word(word, 'n')
for synset in synsets:
vector = get_value_synset_for(synset, synsets)
key = synset.definition()
vectors[key] = vector
return vectors
def get_gloss_for_jacc(word):
vectors = OrderedDict()
synsets = WordnetHandler.get_synsets_for_word(word, 'n')
for synset in synsets:
vector = get_gloss_synset_for(synset)
key = synset.definition()
vectors[key] = vector
return vectors
def get_dict_vectors_words_for_word(word):
vectors = OrderedDict()
synsets = WordnetHandler.get_synsets_for_word(word, 'n')
for synset in synsets:
definition = synset.definition()
vector = PreprocessDefinition.preprocess_sentence(definition)
key = synset.definition()
vectors[key] = vector
return vectors
def sim_ox_wn_defi_WDS_via_1_main_syn(word): dict_vectors_wn = WordnetParseDefinition.get_dict_vectors_synsets_for_word(word) synsets_wn = WordnetHandler.get_synsets_for_word(word,'n') dict_vectors_ox = OxParseDefinition.get_dict_vectors_synsets_for_word(word, synsets_wn) (keys_wn, vectors_wn) = Util.get_keys_values_of_dict(dict_vectors_wn) (keys_ox, vectors_ox) = Util.get_keys_values_of_dict(dict_vectors_ox) m2d_sim = sim_wn_ox_vector(vectors_ox, vectors_wn) return m2d_sim
def get_greatest_synset_similarity_between(synset_1, noun_2):
synset_max = None
(word, pos) = noun_2
synsets_of_noun = []
# if pos_is_noun(pos):
# synsets_of_noun = WordnetHandler.get_synsets_for_word(word, 'n')
# if pos_is_verb(pos):
# synsets_of_noun = WordnetHandler.get_synsets_for_word(word, 'v')
synsets_of_noun_1 = WordnetHandler.get_synsets_for_word(word, 'n')
synsets_of_noun_2 = WordnetHandler.get_synsets_for_word(word, 'v')
synsets_of_noun = synsets_of_noun_1 + synsets_of_noun_2
total_count = 0.1 + len(synsets_of_noun)*__SMOOTH_WEIGHT__
for synset_of_noun in synsets_of_noun:
total_count += WordnetHandler.get_freq_count_of_synset(synset_of_noun)
#
if len(synsets_of_noun) > 0:
synset_max = synsets_of_noun[0]
# p_max = -1.0
#
# for synset_of_noun in synsets_of_noun:
## p = synset_1.path_similarity(synset_of_noun)
# p = WordnetHandler.cal_similarity(synset_1, synset_of_noun)
#
# if p is not None:
# synset_freq_count = __SMOOTH_WEIGHT__
# synset_freq_count += WordnetHandler.get_freq_count_of_synset(synset_of_noun)
#
# p = p*(synset_freq_count/total_count)
#
##
# if p > p_max:
# p_max = p
# synset_max = synset_of_noun
#
return synset_max
def sim_ox_wn_defi_WDS_via_align(word):
words_wn = WordnetParseDefinition.get_dict_vectors_words_for_word(word)
(keys_wn, vectors_wn) = Util.get_keys_values_of_dict(words_wn)
words_ox = OxParseDefinition.get_dict_vectors_word_for_word(word)
(keys_ox, vectors_ox) = Util.get_keys_values_of_dict(words_ox)
synsets_wn = WordnetHandler.get_synsets_for_word(word, 'n')
m2d_sim = [[0 for x in range(len(keys_ox))] for x in range(len(keys_wn))]
for i in range(len(keys_wn)):
vector_wn = vectors_wn[i]
synset_wn = synsets_wn[i]
for j in range(len(keys_ox)):
vector_ox = vectors_ox[j]
m2d_sim[i][j] = WordnetHandler.sim_for_words_words_no_order(vector_ox, vector_wn, synset_wn)
# (vector_1, vector_2) = WordnetHandler.get_nearest_synsets_words_words_noorder(vector_ox, vector_wn)
# m2d_sim[i][j] = sim_2_vector(vector_1, vector_2)
cal_sim_ngrams(word)
return m2d_sim
def sim_ox_wn_defi_WDS_via_curr_main_syn(word):
dict_vectors_wn = WordnetParseDefinition.get_dict_vectors_synsets_for_word(word)
(keys_wn, vectors_wn) = Util.get_keys_values_of_dict(dict_vectors_wn)
synsets_wn = WordnetHandler.get_synsets_for_word(word, 'n')
definitions = OxfordParser.get_definitions_of_word(word)
m2d_sim = [[0 for x in range(len(definitions))] for x in range(len(vectors_wn))]
for i in range(len(vectors_wn)):
vector_wn = vectors_wn[i]
dict_vectors_ox = OxParseDefinition.get_dict_vectors_synsets_for_word(word, [synsets_wn[i]])
(keys_ox, vectors_ox) = Util.get_keys_values_of_dict(dict_vectors_ox)
for j in range(len(vectors_ox)):
vector_ox = vectors_ox[j]
m2d_sim[i][j] = sim_2_vector(vector_ox, vector_wn)
cal_sim_ngrams(word)
return m2d_sim
def create_input_for_test_svm():
dict_ox = OxfordParser.get_dict_nouns()
flag_can_go = False
for word in dict_ox:
# if word == "brook":
# flag_can_go = True
#
# if flag_can_go == False:
# continue
if len(dict_ox[word]) == 0:
continue
syns_wn = WordnetHandler.get_synsets_for_word(word, 'n')
syns_ox = dict_ox[word]
if len(syns_ox) == 1 and len(syns_wn) == 1:
continue
write_sens_for_reading(syns_wn, syns_ox, __filename_input_sen_test__)
cal_features_and_write_to_file_for(syns_wn, syns_ox, __filename_input_test_feature_values__)
def similarity_nbest_withword_average(WORD, dict_words):
wn_words = WordnetHandler.get_synsets_for_word(WORD, 'n');
matrix_similarity = similarity_by_synsets_synsets_nbest_withword_dict_wn(WORD, dict_words,wn_words)
if matrix_similarity == None:
return [],[]
matrix_similarity_reverse = similarity_by_synsets_synsets_nbest_withword_wn_dict(WORD , wn_words, dict_words)
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;
matrix_similarity_jaccard = similarity_by_jaccard(WORD, dict_words, wn_words)
for iWnWord in range(len(wn_words)):
for iDictWord in range(len(dict_words)):
matrix_similarity[iWnWord][iDictWord] = matrix_similarity[iWnWord][iDictWord]*(1-PARAMETERS.JACCARD_WEIGHT) + PARAMETERS.JACCARD_WEIGHT*(1-matrix_similarity_jaccard[iWnWord][iDictWord]);
return matrix_similarity, wn_words
def create_input_for_train_svm():
dict_ox = OxfordParser.get_dict_nouns()
dict_gold = CompareWithGold.goldData
for word in dict_ox:
if len(dict_ox[word]) == 0 or word not in dict_gold:
continue
if word == "brook":
return
# if word != "bank":
# continue
syns_wn = WordnetHandler.get_synsets_for_word(word, 'n')
syns_ox = dict_ox[word]
if len(syns_ox) == 1 and len(syns_wn) == 1:
continue
write_label_for_svm(syns_wn, syns_ox, dict_gold[word])
write_sens_for_reading(syns_wn, syns_ox, __filename_input_sen_train__)
cal_features_and_write_to_file_for(syns_wn, syns_ox, __filename_input_train_feature_values__)
def pair_0_1_reducing_m2d_sim(matrix_similarity, num_rows, num_cols, word):
if num_rows == 1 and num_cols == 1 and matrix_similarity[0][0] > Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_1_MIN:
matrix_similarity[0][0] = 1;
if num_rows > 1 and num_cols == 1:
col = []
for iWnWord in range(num_rows):
col.append(matrix_similarity[iWnWord][0])
order = heapq.nlargest(2, range(num_rows), col.__getitem__);
if matrix_similarity[order[0]][0] >= Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_RANGE_FIRST*matrix_similarity[order[1]][0] or \
matrix_similarity[order[0]][0] > Parameters.PARAMETERS_CHOICE_0_1.CHOICE_1_COL_MIN_FIRST:
matrix_similarity[order[0]][0] = 1;
if num_rows >= 1 and num_cols > 1:
synsets_wn = WordnetHandler.get_synsets_for_word(word,'n')
status_synsets = create_status_array(synsets_wn)
updated = reducing_m2d_sim(matrix_similarity, status_synsets)
while updated == 1:
m2d = sim_ox_wn_defi_WDS_via_main_syns_for_reduce(synsets_wn, status_synsets, word)
updated = reducing_m2d_sim(m2d, status_synsets)
match_matrix_sim_with_temp_matrix(matrix_similarity, m2d)
return matrix_similarity
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_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 sim_ox_wn_via_svm():
total_tp = 0.00001
total_tn = 0.00001
total_fn = 0.00001
total_fp = 0.00001
total_pair = 0
dict_ox = OxfordParser.get_dict_nouns()
flag_can_go = False
for word in dict_ox:
# if word == "brook":
# flag_can_go = True
#
# if flag_can_go == False:
# continue
word_syns_ox = dict_ox[word]
wn_synsets = WordnetHandler.get_synsets_for_word(word, "n")
m2d_sim = [[0 for x in range(len(word_syns_ox))] for x in range(len(wn_synsets))]
if len(word_syns_ox) == 1 and len(wn_synsets) == 1:
m2d_sim[0][0] = 1
else:
m2d_sim = get_m2d_sim_for_word_from_svm_result(word)
if m2d_sim == None:
continue
# DebugHandler.print_2d_matrix(m2d_sim)
m2d_sim = choose_pair_0_1(m2d_sim, len(m2d_sim), len(m2d_sim[0]))
# DebugHandler.print_2d_matrix(m2d_sim)
pair = count_pair(m2d_sim)
total_pair += pair
(tp, tn, fn, fp) = CompareWithGold.compareGoldWithResult_without_cal_result(m2d_sim,word)
if tp != -1:
total_tp += tp
total_tn += tn
total_fn += fn
total_fp += fp
precision = total_tp / (total_tp + total_fp)
recall = total_tp / (total_tp + total_fn)
accuracy = (total_tp + total_tn) / (total_tp + total_tn + total_fp + total_fn)
f_score = 0
if precision != 0 or recall != 0:
f_score = 2*(precision*recall)/(precision + recall)
print "total:"
print total_pair
print total_tp
print total_tn
print total_fn
print total_fp
print precision
print recall
print f_score
print accuracy
Parameters.append_result_to_file( precision, recall, f_score, accuracy)
current_params = Parameters.get_current_params()
current_params = copy.deepcopy(current_params)
return f_score, current_params
