def loadarticles():
files = listdir(ARTICLES_FOLDER)
article_ctr = 0
for file in files:
if article_ctr > MAX_ARTICLE_COUNT:
break
try:
# initialize article element to containe sentences
global articles
articles.append([])
article_names.append(file)
# open the file
f = open(ARTICLES_FOLDER + file, 'r')
# get the text and perform string processing
article = f.read().replace("\\n", "").replace("'b'", "").replace(
"b'", "").replace("[", "").replace("]", "")
article = ''.join([i for i in article if not i.isdigit()])
# seperate by comma
sentences = utils.delim_sentences(article)
for sentence in sentences:
articles[article_ctr].append(sentence)
f.close()
except Exception as e:
print("Error in " + file)
print(e)
# increment
article_ctr = article_ctr + 1
def evaluate(testfiletxt, answerfilejson):
f = open(testfiletxt, 'r')
text = f.read().replace("\\n", "").replace("'b'", "").replace("b'", "").replace("[", "").replace("]", "")
sentences = utils.delim_sentences(text)
with open(answerfilejson) as data_ans:
data = json.loads(data_ans.read())
for sentence in sentences:
print(sentence)
def getResults(article, relation):
model = Model(relation)
sentences = utils.delim_sentences(article)
ret_val = []
for sentence in sentences:
classification = model.predict(sentence)
temp_item = {"sentence": sentence, "classification": classification}
ret_val.append(temp_item)
# print(sentence)
# print(classification)
return ret_val
def main(relation):
print('Creating test file for', relation)
f = open('sen_' + relation + '.txt', 'r')
fans = open('ans_file2_' + relation + '.json', 'w')
wordmodel = ModelWords(relation)
text = f.read()
data = []
sentences = utils.delim_sentences(text)
for sentence in sentences:
print('-------\nSen:', sentence)
baseword, subword = wordmodel.getWords(sentence)
print(':::', baseword, subword)
isCorrect = input("Is correct? [ENTER] YES --- [NO] NO")
if len(isCorrect) == 0:
temp = {
'sentence': sentence,
'baseword': baseword,
'subword': subword
}
data.append(temp)
continue
# else:
# r = random.randint(0, 10)
# if (r > 7):
# temp = {'sentence': sentence, 'baseword': baseword, 'subword': subword}
# data.append(temp)
# baseword = input('Enter baseword: ')
# if len(baseword) == 0:
# continue
# subword = input('Enter subword: ')
fans.write("[\n")
for index, item in enumerate(data):
if index == len(data) - 1:
item['sentence'] = item['sentence'].replace('"', '')
to_write = '\t{"baseword": "%s", "subword": "%s", "sentence": "%s"}\n' % (
item['baseword'], item['subword'], item['sentence'])
fans.write(to_write)
else:
item['sentence'] = item['sentence'].replace('"', '')
to_write = '\t{"baseword": "%s", "subword": "%s", "sentence": "%s"},\n' % (
item['baseword'], item['subword'], item['sentence'])
fans.write(to_write)
fans.write("]")
def evaluate(testfiletxt, answerfilejson, relation, logfile):
REL_INDEX = 0
if relation == 'Synonymy':
REL_INDEX = 1
elif relation == 'Hyponym':
REL_INDEX = 2
elif relation == 'Holynym':
REL_INDEX = 3
elif relation == 'Meronym':
REL_INDEX = 4
model = Model(relation)
f = open(testfiletxt, 'r')
text = f.read().replace("\\n", "").replace("'b'",
"").replace("b'", "").replace(
"[", "").replace("]", "")
sentences = utils.delim_sentences(text)
with open(answerfilejson) as data_ans:
data = json.loads(data_ans.read())
print("-------------------------")
print("\n", relation)
score = 0
for sentence in sentences:
print('\n\nClassifying:', sentence)
# print(model_syn.getTotalScore(sentence))
prediction = model.predict(sentence)
sen_type = getType(data, sentence)
print(':: Classification if', relation, ':\t', prediction)
# one is our marker if syn
if sen_type == REL_INDEX:
ans = True
else:
ans = False
print(':: From ANS file is', relation, ':\t', ans)
if ans == prediction:
score = score + 1
print('Match')
print('\n\nScore for', relation, ':', score, '/', len(sentences))
to_write = "%f, %d, %d\n" % (score / len(sentences), score, len(sentences))
logfile.write(to_write)
def getResults(article, relation):
model = Model(relation)
modelwords = ModelWords(relation)
sentences = utils.delim_sentences(article)
ret_val = []
for sentence in sentences:
classification = model.predict(sentence)
baseword, subword = modelwords.getWords(sentence)
temp_item = {"sentence": sentence, "classification": classification, "baseword": baseword, "subword": subword}
ret_val.append(temp_item)
# print(sentence)
# print(classification)
return ret_val
def create_test_file(filepath, filename):
f = open(filepath, 'r')
fileans = 'ANSF' + filename.replace('.txt', '.json')
text = f.read().replace("\\n", "").replace("'b'",
"").replace("b'", "").replace(
"[", "").replace("]", "")
sentences = utils.delim_sentences(text)
data = []
for sentence in sentences:
print(sentence)
sen_type = input(
"Enter type: [0] None, [1] Syn, [2] Hyp, [3] Holy, [4] Mero \nType: "
)
temp = {'sentence': sentence, 'type': sen_type}
data.append(temp)
fans = open(fileans, 'w')
fans.write("[\n")
for index, item in enumerate(data):
if index == len(data) - 1:
to_write = '\t{"type": "%s", "sentence": "%s"}\n' % (
item['type'],
item['sentence'],
)
fans.write(to_write)
else:
to_write = '\t{"type": "%s", "sentence": "%s"},\n' % (
item['type'],
item['sentence'],
)
fans.write(to_write)
fans.write("]")
ftxt = open('TESTF' + filename, 'w')
ftxt.write(text)
ftxt.close()
f.close()
fans.close()
def run():
sentencemodel = ModelSentence('Synonymy')
wordmodel = ModelWords('Synonymy')
fsyn = open('sen_syn.txt', 'w')
# work on the first 10 articles
n = 100
files = os.listdir('./articles')
for i in range(0, len(files)):
f = open('./articles/' + files[i], 'r')
article = f.read()
sentences = utils.delim_sentences(article)
for sentence in sentences:
# A sentence with synonymy is found
if (sentencemodel.predict(sentence)):
to_write = sentence + '\n'
fsyn.write(to_write)
print(files[i], ":", wordmodel.getWords(sentence))
f.close()
def run(relation):
print(relation)
sentencemodel = ModelSentence(relation)
wordmodel = ModelWords(relation)
fsyn = open('sen_' + relation + '.txt', 'w')
# work on the first 10 articles
n = 100
files = os.listdir('./articles')
for i in range(0, n):
file_index = random.randint(0, len(files))
f = open('./articles/' + files[file_index], 'r')
article = f.read()
sentences = utils.delim_sentences(article)
for sentence in sentences:
# A sentence with synonymy is found
if (sentencemodel.predict(sentence)):
to_write = sentence + '.\n'
fsyn.write(to_write)
f.close()