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preprocess.py
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preprocess.py
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# -*- coding:utf-8 -*-
# author: Gene_ZC
import codecs
import gensim
import random
import os
import pynlpir
def make_dev(filename):
with open(filename, 'r', encoding='utf-8') as f:
lst = f.read().split('\n\n')
length = len(lst)
random.shuffle(lst)
lst = lst[:int(length/10)]
with open('data/char_dev.txt', 'w', encoding='utf-8') as dev:
for item in lst:
dev.write(item+'\n\n')
def create_corpus(filename):
f_save = open('data/text8', 'w', encoding='utf-8')
count = 0
with open(filename, 'r', encoding='utf-8') as f:
for line in f:
if line == '\n':
continue
else:
word = line.split(' ')[0]
f_save.write(' '+word)
count += 1
f_save.write('\n')
f_save.close()
print('word count:' + str(count))
def preprocess(filename):
f_save = open('data/char_test.txt', 'w', encoding='utf-8')
pynlpir.open()
with open(filename, 'r', encoding='utf-8') as f:
for line in f:
lst = line.rstrip().split(' ')
for item in lst:
c, t = item.split('/')
if t == 'o':
c = pynlpir.segment(c, pos_tagging=False)
for i, x in enumerate(c):
f_save.write(x+' '+'O'+'\n')
elif t == 'ns':
c = pynlpir.segment(c, pos_tagging=False)
for i, x in enumerate(c):
if i == 0:
f_save.write(x+' '+'B-LOC'+'\n')
else:
f_save.write(x+' '+'I-LOC'+'\n')
elif t == 'nt':
c = pynlpir.segment(c, pos_tagging=False)
for i, x in enumerate(c):
if i == 0:
f_save.write(x+' '+'B-ORG'+'\n')
else:
f_save.write(x+' '+'I-ORG'+'\n')
elif t == 'nr':
c = pynlpir.segment(c, pos_tagging=False)
for i, x in enumerate(c):
if i == 0:
f_save.write(x+' '+'B-PER'+'\n')
else:
f_save.write(x+' '+'I-PER'+'\n')
f_save.write('\n')
f_save.close()
def test():
model = gensim.models.KeyedVectors.load_word2vec_format('fastText/vectors.txt', binary=False)
sim = model.most_similar('hh', topn=10)
print(sim)
if __name__ == '__main__':
# preprocess('data/testright1.txt')
# create_corpus('data/char_train.txt')
# make_dev('data/char_train.txt')
test()