def check_morphs(lst, corpus_fname, output_fname, log_fname):
mcab = mecab.MeCab()
model_fname = 'soyword.model'
word_extractor = WordExtractor(
min_frequency=100,
min_cohesion_forward=0.05,
min_right_branching_entropy=0.0
)
word_extractor.load(model_fname)
scores = word_extractor.word_scores()
scores = {key:(scores[key].cohesion_forward * math.exp(scores[key].right_branching_entropy)) for key in scores.keys()}
soy_tokenizer = LTokenizer(scores=scores)
with open(corpus_fname, 'r', encoding='utf-8') as f1, \
open(output_fname, 'w', encoding='utf-8') as f2, \
open(log_fname, 'w', encoding='utf-8') as f3:
sentences = f1.read()
for item in lst:
cnt, word = item
if cnt < 10 or len(word) == 1:
continue
tokens = mcab.morphs(word)
if len(tokens) == 1:
continue
soy_tokens = soy_tokenizer.tokenize(word)
if ' '.join(tokens) == ' '.join(soy_tokens):
continue
if is_all_nng(mcab.pos(word)):
#print("nouns only : {}".format(word))
#print("{}\t{}\t{}\t{}".format(word, ' '.join(tokens), ' '.join(soy_tokens), cnt))
continue
if len(soy_tokens) > 1:
continue
#print("{}\t{}\t{}\t{}".format(word, ' '.join(tokens), ' '.join(soy_tokens), cnt))
words = re.findall(' '.join(tokens), sentences)
if len(words) < (cnt * 0.05):
# 형태소 분리된 단어의 빈도수가 분리안된 단어의 빈수도의 5% 미만이면 형태소 분리오류
(cho, jung, jong) = hgtk.letter.decompose(word[-1])
if 'ㄱ' <= jong <= 'ㅎ':
dic_line = "{},,,1000,NNP,*,{},{},*,*,*,*,*".format(word, 'T', word)
else:
dic_line = "{},,,1000,NNP,*,{},{},*,*,*,*,*".format(word, 'F', word)
print("{}\t{}\t{}\t{}\t{}\t{}".format(word, ' '.join(tokens), ' '.join(soy_tokens), cnt, len(words), jong))
f2.writelines(dic_line + '\n')
f3.writelines("{}\t{}\t{}\t{}\t{}".format(word, ' '.join(tokens), ' '.join(soy_tokens), cnt, len(words)) + '\n')
def soy_tokenize(model_fname, input_sentence):
word_extractor = WordExtractor(min_frequency=100,
min_cohesion_forward=0.05,
min_right_branching_entropy=0.0)
word_extractor.load(model_fname)
scores = word_extractor.word_scores()
# https://github.com/lovit/soynlp/blob/master/tutorials/wordextractor_lecture.ipynb
# (1) 주어진 글자가 유기적으로 연결되어 함께 자주 나타나고,
# (2) 그 단어의 우측에 다양한 조사, 어미, 혹은 다른 단어가 등장하여 단어의 우측의 branching entropy가 높다
scores = {
key: (scores[key].cohesion_forward *
math.exp(scores[key].right_branching_entropy))
for key in scores.keys()
}
tokenizer = LTokenizer(scores=scores)
tokens = tokenizer.tokenize(input_sentence)
tokenized_sent = ' '.join(tokens)
return tokenized_sent
def soy_tokenize(corpus_fname, model_fname, output_fname):
word_extractor = WordExtractor(min_frequency=100,
min_cohesion_forward=0.05,
min_right_branching_entropy=0.0
)
word_extractor.load(model_fname)
scores = word_extractor.word_scores()
# https://github.com/lovit/soynlp/blob/master/tutorials/wordextractor_lecture.ipynb
# (1) 주어진 글자가 유기적으로 연결되어 함께 자주 나타나고,
# (2) 그 단어의 우측에 다양한 조사, 어미, 혹은 다른 단어가 등장하여 단어의 우측의 branching entropy가 높다
scores = {
key: (scores[key].cohesion_forward * math.exp(scores[key].right_branching_entropy)) for key in scores.keys()
}
tokenizer = LTokenizer(scores=scores)
with open(corpus_fname, 'r', encoding='utf-8') as f1, \
open(output_fname, 'w', encoding='utf-8') as f2:
for line in f1:
sentence = line.replace('\n', '').strip()
normalized_sent = emoticon_normalize(sentence, num_repeats=3)
tokens = tokenizer.tokenize(normalized_sent)
tokenized_sent = ' '.join(tokens)
f2.writelines(tokenized_sent + '\n')
def check_morphs(lst, corpus_fname, output_fname, log_fname):
mcab = mecab.MeCab()
model_fname = 'soyword.model'
word_extractor = WordExtractor(
min_frequency=100,
min_cohesion_forward=0.05,
min_right_branching_entropy=0.0
)
word_extractor.load(model_fname)
scores = word_extractor.word_scores()
scores = {key:(scores[key].cohesion_forward * math.exp(scores[key].right_branching_entropy)) for key in scores.keys()}
soy_tokenizer = LTokenizer(scores=scores)
with open(corpus_fname, 'r', encoding='utf-8') as f1, \
open(output_fname, 'w', encoding='utf-8') as f2, \
open(log_fname, 'w', encoding='utf-8') as f3:
sentences = f1.read()
for item in lst:
cnt, word = item
if cnt < 100 or len(word) == 1:
continue
tokens = mcab.morphs(word)
if len(tokens) == 1:
continue
(cho, jung, jong) = hgtk.letter.decompose(word[-1])
if 'ㄱ' <= jong <= 'ㅎ':
dic_line = "{},,,,NNP,*,{},{},*,*,*,*,*".format(word, 'T', word)
else:
dic_line = "{},,,,NNP,*,{},{},*,*,*,*,*".format(word, 'F', word)
f2.writelines(dic_line + '\n')
f3.writelines("{}\t{}\t{}".format(word, ' '.join(tokens), cnt) + '\n')
temp = []
for p in pos:
temp.append(p[0] + "/" + p[1])
s = ' '.join(temp)
return s
etri_processed_data["title"] = etri_processed_data["title"].progress_apply(concat_text_with_pos)
word_extractor = WordExtractor(
min_frequency=100,
min_cohesion_forward=0.05,
min_right_branching_entropy=0.0
)
soynlp_model_fname = './backend/textengines/data/tokenizer_model/soyword.model'
word_extractor.load(soynlp_model_fname)
scores = word_extractor.word_scores()
scores = {key:(scores[key].cohesion_forward * math.exp(scores[key].right_branching_entropy)) for key in scores.keys()}
soyToken = LTokenizer(scores=scores)
# soyToken.tokenize(soynlp_processed_data["title"].values[0])
soynlp_processed_data["title"] = soynlp_processed_data["title"].progress_apply(lambda x: " ".join(soyToken.tokenize(x)))
token = spm.SentencePieceProcessor()
token.Load("./backend/textengines/data/tokenizer_model/sentencepice.model")
spm_processed_data["title"] = spm_processed_data["title"].progress_apply(lambda x: " ".join(token.EncodeAsPieces(x)))
#############################################################################
td = etri_processed_data.copy()
ratio_train = 0.8
ratio_val = 0.1
