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dataset_preprocess.py
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dataset_preprocess.py
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import os
import copy
def is_similar(s1, s2):
count = 0.0
for token in s1.split(' '):
if token in s2:
count += 1
if count / len(s1.split(' ')) >= 0.9 and count / len(s2.split(' ')) >= 0.9:
return True
else:
return False
def assemble_aspects(fname):
fin = open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore')
lines = fin.readlines()
fin.close()
for i in range(len(lines)):
lines[i] = lines[i].replace('$ t $','$T$').strip()
def unify_same_samples(same_samples):
text = same_samples[0][0].replace('$T$', same_samples[0][1])
polarities = [-1]*len(text.split())
tags=['O']*len(text.split())
samples = []
for sample in same_samples:
# print(sample)
polarities_tmp = copy.deepcopy(polarities)
try:
asp_begin = (sample[0].split().index('$T$'))
asp_end = sample[0].split().index('$T$')+len(sample[1].split())
for i in range(asp_begin, asp_end):
polarities_tmp[i] = int(sample[2])+1
if i - sample[0].split().index('$T$')<1:
tags[i] = 'B-ASP'
else:
tags[i] = 'I-ASP'
samples.append([text, tags, polarities_tmp])
except:
print(sample[0])
return samples
samples = []
aspects_in_one_sentence = []
for i in range(0, len(lines), 3):
# aspects_in_one_sentence.append([lines[i], lines[i + 1], lines[i + 2]])
if len(aspects_in_one_sentence) == 0:
aspects_in_one_sentence.append([lines[i], lines[i + 1], lines[i + 2]])
continue
if is_similar(aspects_in_one_sentence[-1][0], lines[i]):
aspects_in_one_sentence.append([lines[i], lines[i + 1], lines[i + 2]])
else:
samples.extend(unify_same_samples(aspects_in_one_sentence))
aspects_in_one_sentence = []
aspects_in_one_sentence.append([lines[i], lines[i + 1], lines[i + 2]])
return samples
def split_aspects(sentence):
single_aspect_with_contex = []
aspect_num = len(sentence[1].split("|"))
aspects = sentence[1].split("|")
polarity = sentence[2].split("|")
pre_position = 0
aspect_contex = sentence[0]
for i in range(aspect_num):
aspect_contex = aspect_contex.replace("$A$", aspects[i], 1)
single_aspect_with_contex.append(
(aspect_contex[pre_position:aspect_contex.find("$A$")], aspects[i], polarity[i]))
pre_position = aspect_contex.find(aspects[i]) + len(aspects[i]) + 1
return single_aspect_with_contex
# 将数据集中的aspect切割出来
def refactor_dataset(fname, dist_fname):
lines = []
samples = assemble_aspects(fname)
for sample in samples:
for token_index in range(len(sample[1])):
token, label, polarty = sample[0].split()[token_index], sample[1][token_index], sample[2][token_index]
lines.append(token + " " + label + " " + str(polarty))
lines.append('\n')
# 写之前，先检验文件是否存在，存在就删掉
if os.path.exists(dist_fname):
os.remove(dist_fname)
fout = open(dist_fname, 'w', encoding='utf8')
for line in lines:
fout.writelines((line+'\n').replace('\n\n', '\n'))
fout.close()
# 将数据集中的aspect切割出来
def refactor_chinese_dataset(fname, train_fname,test_fname):
lines = []
samples = assemble_aspects(fname)
positive = 0
negative = 0
sum = 0
# refactor testset
for sample in samples[:int(len(samples)/5)]:
for token_index in range(len(sample[1])):
token, label, polarty = sample[0].split()[token_index], sample[1][token_index], sample[2][token_index]
lines.append(token + " " + label + " " + str(polarty))
lines.append('\n')
if 1 in sample[2]:
positive+=1
else:negative+=1
sum+=1
print(train_fname+f"sum={sum} positive={positive} negative={negative}")
if os.path.exists(test_fname):
os.remove(test_fname)
fout = open(test_fname, 'w', encoding='utf8')
for line in lines:
fout.writelines((line+'\n').replace('\n\n', '\n'))
fout.close()
positive = 0
negative = 0
sum = 0
# refactor trainset
for sample in samples[int(len(samples)/5):]:
for token_index in range(len(sample[1])):
tokens = sample[0].split()
token, label, polarty = sample[0].split()[token_index], sample[1][token_index], sample[2][token_index]
lines.append(token + " " + label + " " + str(polarty))
lines.append('\n')
if 1 in sample[2]:
positive+=1
else:negative+=1
sum+=1
print(train_fname+f"sum={sum} positive={positive} negative={negative}")
if os.path.exists(train_fname):
os.remove(train_fname)
fout = open(train_fname, 'w', encoding='utf8')
for line in lines:
fout.writelines((line + '\n').replace('\n\n', '\n'))
fout.close()
def detect_error_in_dataset(dataset):
f = open(dataset, 'r', encoding='utf8')
lines = f.readlines()
for i in range(0, len(lines), 3):
# print(lines[i].replace('$T$', lines[i + 1].replace('\n', '')))
if i + 3 < len(lines):
if is_similar(lines[i],lines[i+3]) and len((lines[i]+" "+ lines[i+1]).split()) != len((lines[i+3]+" "+ lines[i+4]).split()):
print(lines[i].replace('$T$', lines[i+1].replace('\n','')))
print(lines[i+3].replace('$T$', lines[i+4].replace('\n','')))
if __name__ == "__main__":
# convert datasets of aspect polarity classification (apc) for atepc
# find the apc datasets at https://github.com/yangheng95/LC-ABSA/tree/master/datasets
# 笔记本数据集
refactor_dataset(
r"../datasets_origin/semeval14/Laptops_Train.xml.seg",
r"../atepc_datasets/laptop/Laptops.atepc.train.dat",
)
refactor_dataset(
r"../datasets_origin/semeval14/Laptops_Test_Gold.xml.seg",
r"../atepc_datasets/laptop/Laptops.atepc.test.dat",
)
# # 餐厅数据集
refactor_dataset(
r"../datasets_origin/semeval14/Restaurants_Train.xml.seg",
r"../atepc_datasets/restaurant/Restaurants.atepc.train.dat",
)
refactor_dataset(
r"../datasets_origin/semeval14/Restaurants_Test_Gold.xml.seg",
r"../atepc_datasets/restaurant/Restaurants.atepc.test.dat",
)
# 推特数据集
refactor_dataset(
r"../datasets_origin/acl-14-short-data/train.raw",
r"../atepc_datasets/twitter/twitter.atepc.train.dat",
)
refactor_dataset(
r"../datasets_origin/acl-14-short-data/test.raw",
r"../atepc_datasets/twitter/twitter.atepc.test.dat",
)