def read_unstructure_texts(files):
texts = []
for file in files:
with open(file, encoding='utf8') as f:
data = f.read()
data = stemming(remove_stop_words(tokenize(data)))
texts.append(data)
return texts
def read_to_texts(files):
texts = []
for file in files:
with open(file, encoding='utf8') as f:
data = f.read()
parsed_result = parse_paper(data)
text = ''
for section, content in parsed_result['structure'].items():
text += content
text = stemming(remove_stop_words(tokenize(text)))
texts.append(text)
return texts
def load_file(path):
texts = []
num_files = 0
num_methods = 0
num_abstract = 0
paper_attributes = {}
country_counter = []
for folder in os.listdir(path):
for file in os.listdir(os.path.join(path, folder)):
num_files += 1
if num_files <= NUM_START:
continue
parsed_result = file2text(os.path.join(path, folder, file))
method_text = get_methods(parsed_result['text'])
if method_text:
token_text = stemming(remove_stop_words(tokenize(method_text)))
texts.append(token_text)
num_methods += 1
author_countries = get_country(parsed_result['text'][:1000])
if len(author_countries) > 1:
num_abstract += 1
paper_id = file.split('.')[0]
paper_attributes[paper_id] = {'countries': author_countries}
for c in author_countries:
if c in country_counter:
country_counter[c] += 1
else:
country_counter[c] = 1
if num_files % 1000 == 0:
print(num_files, num_methods, num_abstract)
if num_files > NUM_STOP:
with open(os.path.join(os.getcwd(), 'metadata', OUTPUT_FILE), 'w') as f:
json.dump(paper_attributes, f)
print(country_counter)
return
print("finished extraction")
print(num_files, num_methods, num_abstract)
def tokenize_text(text):
token_text = stemming(remove_stop_words(tokenize(text)))
filetered_text = remove_lf_words(token_text, 2)
return filetered_text
