def similarityWords(dictOxfordNouns):
print "\n --------------------------------------------------"
print "new round"
total_precision = 0;
total_recall = 0;
total_accuracy = 0;
missingWORD = 0;
total_word = 0
count_step = 0
for word in dictOxfordNouns:
# print dictOxfordNouns[word]
# if word == 'baby':
matrix_result = similarity_by_synsets_synsets_nbest_withword_average(word,dictOxfordNouns[word]);
if matrix_result == None:
missingWORD += 1;
continue
# print "\n"
print word
(precision, recall, accuracy) = CompareWithGold.compareGoldWithResult(matrix_result,word)
# print (precision, recall, accuracy)
if precision != -1:
total_precision += precision
total_recall += recall
total_accuracy += accuracy
total_word += 1
count_step += 1
# print "result -------"
# print precision
# print recall
# print accuracy
# print "\n"
else:
missingWORD += 1
# total_word = len(dictOxfordNouns) - missingWORD
precision = total_precision/total_word
recall = total_recall/total_word
accuracy = total_accuracy/total_word
print "total:"
print total_word
print precision
print recall
print accuracy
WriteParametersAndResult.append_result_to_file(precision,recall,accuracy)
return (precision, recall, accuracy)
def sim_ox_wn_via_definition_cal_word():
total_precision = 0;
total_recall = 0;
total_accuracy = 0;
total_word = 0
dict_ox = OxfordParser.get_dict_nouns()
for word in dict_ox:
if word not in __m2d_sim__:
m2d_sim = sim_ox_wn_definition(word)
__m2d_sim__[word] = m2d_sim
m2d_sim = copy.deepcopy(__m2d_sim__[word])
if m2d_sim == None or len(m2d_sim) == 0 or len(m2d_sim[0]) == 0:
continue
print word
#
# if len(m2d_sim) == 1 and len(m2d_sim[0]) == 1:
# continue
#
m2d_sim = choose_pair_0_1(m2d_sim, len(m2d_sim), len(m2d_sim[0]))
(precision, recall, accuracy) = CompareWithGold.compareGoldWithResult(m2d_sim,word)
if precision != -1:
total_precision += precision
total_recall += recall
total_accuracy += accuracy
total_word += 1
precision = total_precision/total_word
recall = total_recall/total_word
f_score = 0
if precision != 0 or recall != 0:
f_score = 2*(precision*recall)/(precision + recall)
accuracy = total_accuracy/total_word
print "total:"
print total_word
print precision
print recall
print f_score
print accuracy
Parameters.append_result_to_file( precision, recall, f_score, accuracy)
