๐Ÿ“š ์Šคํ„ฐ๋””/NLP

[ํ…์ŠคํŠธ๋งˆ์ด๋‹] 2-2. ๋ฌธ์„œ ์œ ์‚ฌ๋„ - ์Šคํƒ€๋ฒ…์Šค ๋„ค์ด๋ฒ„ ๋ฌธ์„œ ์œ ์‚ฌ๋„ ๊ตฌํ•˜๊ธฐ

2023. 6. 19. 18:05
๋ชฉ์ฐจ
  1. 1. ๋ฐ์ดํ„ฐ ํ™•์ธ ๋ฐ ์ „์ฒ˜๋ฆฌ
  2. 2. TF-IDF ์ ์šฉ
  3. 3. ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„ ๊ตฌํ•˜๊ธฐ
  4. 4. ๊ตฐ์ง‘ํ™”

์ง€๋‚œ๋ฒˆ์— ๋ฐฐ์šด BoW, DTM, TF-IDF, ์œ ํด๋ฆฌ๋””์•ˆ ์œ ์‚ฌ๋„, ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ง์ ‘ ํฌ๋กค๋งํ•œ '์Šคํƒ€๋ฒ…์Šค' ๊ด€๋ จ ๊ธฐ์‚ฌ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ๋ฌธ์„œ ์œ ์‚ฌ๋„๋ฅผ ๊ตฌํ•˜๋Š” ์‹ค์Šต์„ ์ง„ํ–‰ํ•ด ๋ณด์•˜๋‹ค.

์ž์„ธํ•œ ์ฝ”๋“œ๋“ค์€ ๊นƒํ—ˆ๋ธŒ๋ฅผ ์ฐธ๊ณ ํ•˜๊ธธ ๋ฐ”๋ž€๋‹ค.


1. ๋ฐ์ดํ„ฐ ํ™•์ธ ๋ฐ ์ „์ฒ˜๋ฆฌ

๋ฐ์ดํ„ฐ๋ฅผ df๋ผ๋Š” ๋ณ€์ˆ˜์— ์ €์žฅํ•˜๊ณ  ํ™•์ธํ•ด ๋ณด์•˜๋‹ค.

df.head()
์ œ๋ชฉ ์–ธ๋ก ์‚ฌ ๋‚ ์งœ URL ๋„ค์ด๋ฒ„๋‰ด์Šค_URL
์˜ค๋Š˜(1/1) ์ฝ”์ŠคํŠธ์ฝ” ์ •์ƒ์˜์—…, ์ง€์ ๋ณ„ 1์›” ํœด๋ฌด์ผยท์˜์—…์‹œ๊ฐ„ 'ํ™•์ธํ•˜์„ธ์š”' ํ•€ํฌ์ธํŠธ๋‰ด์Šค 2023.01.01. http://www.pinpointnews.co.kr/news/articleView... NaN
ํ˜ธ๋ž‘์ด ๊ฐ€๊ณ  ๊ฒ€์€ ํ† ๋ผ ์˜จ๋‹คโ€ฆ์œ ํ†ต๊ฐ€ โ€˜ํ† ๋ผ ๋งˆ์ผ€ํŒ…โ€™ ํ™œ๋ฐœ ์ธ๋”๋‰ด์Šค 2023.01.01. https://www.inthenews.co.kr/news/article.html?... NaN
[์•„๋“€2022 โ‘ก] ๋จธ์ง€๋ถ€ํ„ฐ FTX ํŒŒ์‚ฐ๊นŒ์ง€...์˜ฌํ•ด์˜ ์ฃผ์š” ์ด์Šˆ TOP 10 ํ† ํฐํฌ์ŠคํŠธ 2023.01.01. https://www.tokenpost.kr/article-117836 NaN
๊ฐ•๋‚จ ์ง‘ ํŒ”์•„ ์ฒญ์†Œ๋…„ ์‰ผํ„ฐ ์„ธ์› ๋‹คโ€ฆ๋ฐ”๋ฆฌ์Šคํƒ€ ํ‚ค์šฐ๋Š” ํšŒ์žฅ๋‹˜ ์ค‘์•™์ผ๋ณด 2023.01.01. https://www.joongang.co.kr/article/25130324 https://n.news.naver.com/mnews/article/025/000...
๋ณ€ํ™”ํ•˜๋Š” ์œ ํ†ต์—…๊ณ„, ์˜์—ญ๊ณผ ๊ฒฝ๊ณ„ ํ—ˆ๋ฌธ 'ํŒŒ๊ดด์  ์ปค๋จธ์Šค' ์‹œ๋Œ€ ์—ด๋ ธ๋‹ค ๋‰ด์Šค1 2023.01.01. https://www.news1.kr/articles/4865044 https://n.news.naver.com/mnews/article/421/000...

ํฌ๋กค๋งํ•˜๋Š” ๊ณผ์ •์—์„œ URL์ด ์ค‘๋ณต๋์„ ์ˆ˜๋„ ์žˆ๊ฒ ๋‹ค๊ณ  ์ƒ๊ฐํ•˜์—ฌ ์ค‘๋ณต๊ฐ’์„ ํ™•์ธ ํ›„, ์ œ๊ฑฐ, index ์žฌ์„ค์ •๊นŒ์ง€ ํ•ด์ฃผ์—ˆ๋‹ค.

# URL ๊ธฐ์ค€ ์ค‘๋ณต๊ฐ’ ํ™•์ธ
print(df.duplicated().sum())
print(df.duplicated(subset='URL').sum())

df[df.duplicated(subset='URL')]

# URL ๊ธฐ์ค€ ์ค‘๋ณต๊ฐ’ ์ œ๊ฑฐ
df.drop_duplicates(subset='URL', inplace=True)

# ์ธ๋ฑ์Šค ์žฌ์„ค์ •
df.reset_index(drop=True, inplace=True)

์ดํ›„, Okt ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ œ๋ชฉ์— ์žˆ๋Š” ๋‹จ์–ด๋“ค์„ ๋ช…์‚ฌ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ํ† ํฐํ™”์‹œํ‚ค๊ณ , ๋ณ‘ํ•ฉํ•˜์—ฌ ํ•˜๋‚˜์˜ ๋ฆฌ์ŠคํŠธ๋กœ ๋งŒ๋“ค์–ด ๋ณด์•˜๋‹ค.

์ดํ›„, CounterVecorizer ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ€์žฅ ๋งŽ์ด ๋“ฑ์žฅํ•œ ๋‹จ์–ด๋ฅผ ์ถœ๋ ฅํ•ด ๋ณด์•˜๋‹ค.

# Okt ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
from konlpy.tag import Okt
okt = Okt()

# ๋ช…์‚ฌ ์ค‘์‹ฌ์˜ ํ† ํฐํ™”
df['๋‹จ์–ด'] = df['์ œ๋ชฉ'].map(okt.nouns)

# ๋‹จ์–ด ๋ฆฌ์ŠคํŠธ ๋งŒ๋“ค๊ธฐ
word_list = sum(df['๋‹จ์–ด'],[])

# Counter ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
from collections import Counter
c = Counter(word_list)

# ๊ฐ€์žฅ ๋งŽ์ด ๋“ฑ์žฅํ•œ ๋‹จ์–ด
c.most_common(100)

์ถœ๋ ฅ๊ฐ’

์ถœ๋ ฅ๊ฐ’์„ ํ™•์ธํ•ด ๋ณด๋‹ˆ, ์ƒ๊ฐ๋ณด๋‹ค '์Šคํƒ€๋ฒ…์Šค'์™€ ๊ด€๋ จ๋œ ๋‹จ์–ด๊ฐ€ ๊ฑฐ์˜ ์—†์—ˆ๋‹ค. ์ž ์‹œ ์ฆ๊ถŒ์‚ฌ ํฌ๋กค๋ง ๋ฐ์ดํ„ฐ๋ฅผ ์ž˜๋ชป ๊ฐ€์ ธ์™”๋‚˜ ์‹ถ๊ธฐ๋„ ํ–ˆ๋‹ค. ํ•˜์ง€๋งŒ ์‹ค์ œ๋กœ ๋„ค์ด๋ฒ„ ๊ฒ€์ƒ‰์—์„œ ๊ธฐ์‚ฌ๋“ค์„ ํ™•์ธํ•ด ๋ณด๋‹ˆ ์œ„ ๋‹จ์–ด๋“ค์ด ๋“ค์–ด๊ฐ„ ๊ธฐ์‚ฌ๋“ค์ด ์ •๋ง ๋งŽ์•˜๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด '์Šคํƒ€๋ฒ…์Šค' ๋˜๋Š” '์ปคํ”ผ'๊ฐ€ ๋“ค์–ด๊ฐ„ ๋‹จ์–ด๋“ค๋กœ๋งŒ ํ•„ํ„ฐ๋ง์„ ์ง„ํ–‰ํ–ˆ๋‹ค.

df = df[df['๋‹จ์–ด'].map(lambda x: '์Šคํƒ€๋ฒ…์Šค' in x or '์ปคํ”ผ' in x)].copy(); df

์ถœ๋ ฅ๊ฐ’

์กฐ๊ธˆ์€ ์ปคํ”ผ์™€ ๊ด€๋ จ๋œ ๊ธฐ์‚ฌ๋“ค์ด ๋งŽ์•„์ง„ ๊ฒƒ ๊ฐ™์•„ ๋ณด์˜€๋‹ค.

์ดํ›„, ์ œ๋ชฉ์— ๋“ค์–ด๊ฐˆ ๋‹จ์–ด๋“ค์„ ์ „์ฒ˜๋ฆฌํ•˜์—ฌ '์ œ๋ชฉ_์ „์ฒ˜๋ฆฌ'๋ผ๋Š” ์ œ๋ชฉ์˜ ํ–‰์œผ๋กœ ๋‹ค์‹œ ์ €์žฅํ–ˆ๋‹ค.

# ํ…์ŠคํŠธ ํด๋ฆฌ๋‹
import re
df['์ œ๋ชฉ_์ „์ฒ˜๋ฆฌ'] = df['์ œ๋ชฉ'].map(lambda x: re.sub('[^\w\s]', ' ', x)) 

# ์ธ๋ฑ์Šค ๋ฆฌ์…‹
df.reset_index(drop=True, inplace=True)

2. TF-IDF ์ ์šฉ

๋‹ค์Œ์œผ๋กœ๋Š” TF-IDF ๋ฐฉ์‹์œผ๋กœ ๋‹จ์–ด๋“ค์„ ๋ฒกํ„ฐํ™”์‹œ์ผœ DTM์„ ์ƒ์„ฑํ•ด ๋ณด์•˜๋‹ค. (์‚ฌ์ดํ‚ท๋Ÿฐ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค.)

# TF-IDF ๋ฒกํ„ฐํ™”
vectorizer = TfidfVectorizer(min_df = 10, ngram_range=(1,2), tokenizer=okt.morphs)
features = vectorizer.fit_transform(df['์ œ๋ชฉ_์ „์ฒ˜๋ฆฌ'])

# feature ์ด๋ฆ„ ๋ถˆ๋Ÿฌ ์˜ค๊ธฐ
feature_names = vectorizer.get_feature_names_out()

# DTM ์ƒ์„ฑ
dtm_np = np.array(features.todense())

# DataFrame ํ™•์ธ
pd.DataFrame(data = dtm_np, columns = feature_names)

์ถœ๋ ฅ๊ฐ’์€ ๋‹ค์Œ๊ณผ ๊ฐ™์•˜๋‹ค.

๋น„์Šทํ•œ ๋‚ด์šฉ์˜ ๊ธฐ์‚ฌ๋“ค๋กœ ๊ตฐ์ง‘ํ™”์‹œํ‚ค๋Š” ๊ฒƒ์ด ์ตœ์ข… ๋ชฉํ‘œ์ด๊ธฐ ๋•Œ๋ฌธ์—, ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„๋ฅผ ํ†ตํ•ด ์œ ์‚ฌ๋„๋ฅผ ๊ตฌํ•ด๋ณด์•˜๋‹ค.

3. ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„ ๊ตฌํ•˜๊ธฐ

์‚ฌ์ดํ‚ท๋Ÿฐ์˜ cosine_similarity๋ฅผ ์‚ฌ์šฉํ–ˆ๋‹ค.

์ดํ›„ ํ•˜๋‚˜์˜ ๊ธฐ์‚ฌ๋ฅผ ์„ ์ •ํ•ด์„œ ํ•ด๋‹น ๊ธฐ์‚ฌ์™€ ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„๊ฐ€ ๋†’์€ top 10 ๊ธฐ์‚ฌ๋“ค์„ ์ถœ๋ ฅํ•ด ๋ณด์•˜๋‹ค.

# ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„ ๊ตฌํ•˜๊ธฐ
from sklearn.metrics.pairwise import cosine_similarity
cosine_sim = cosine_similarity(dtm_np, dtm_np)

# ์ธ๋ฑ์Šค ์„ค์ •
indices = pd.Series(df.index, index=df['์ œ๋ชฉ'])

# ๋‰ด์Šค ๊ธฐ์‚ฌ ์ธ๋ฑ์Šค ์ถ”์ถœ
idx = indices["์Šคํƒ€๋ฒ…์Šค, ์˜ฌํ•ด ์ปค๋ฎค๋‹ˆํ‹ฐ ์Šคํ† ์–ด ์ฒญ๋…„์ธ์žฌ ๋ฐฐ์ถœโ€ฆ'์—ญ๋Œ€ ์ตœ๋‹ค ์ธ์›'"]
print(idx) # 1753

# ์„ ํƒํ•œ ๋‰ด์Šค ๊ธฐ์‚ฌ์™€ ๋‹ค๋ฅธ ๋‰ด์Šค ๊ธฐ์‚ฌ๊ฐ„ ์œ ์‚ฌ๋„ ํ™•์ธ
sim_scores = list(enumerate(cosine_sim[idx]))

# ์œ ์‚ฌ๋„๊ฐ€ ๋†’์€ ์ˆœ์œผ๋กœ ๋‰ด์Šค ์ •๋ ฌ
sim_scores = sorted(sim_scores, key = lambda x: x[1], reverse=True)

# ์œ ์‚ฌ๋„ ๋†’์€ 10๊ฐœ์˜ ๋‰ด์Šค
sim_scores = sim_scores[0:11]; sim_scores # 0์€ ์ž๊ธฐ ์ž์‹ ์ผ ๊ฒƒ

์ถœ๋ ฅ๊ฐ’

๋‹น์—ฐํžˆ ๊ฐ™์€ ๊ธฐ์‚ฌ๋ผ๋ฆฌ๋Š” 1์˜ ์œ ์‚ฌ๋„๋ฅผ ๋ณด์˜€๋‹ค.

์ฒซ ๊ธฐ์‚ฌ๋ฅผ ์ œ์™ธํ•˜๊ณ , ๋‚˜๋จธ์ง€ ๊ธฐ์‚ฌ๋“ค๋กœ ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„ ๊ธฐ๋ฐ˜์˜ ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„์„ ๋งŒ๋“ค์–ด ๋ณด์•˜๋‹ค.

sim_scores = sim_scores[1:11]

# ๊ฐ€์žฅ ์œ ์‚ฌํ•œ 10๊ฐœ์˜ ๋‰ด์Šค์˜ ์ธ๋ฑ์Šค
news_indices = [i[0] for i in sim_scores]

# ์œ ์‚ฌํ•œ ๋‰ด์Šค ๊ธฐ์‚ฌ ์ œ๋ชฉ ์ถœ๋ ฅ
df['์ œ๋ชฉ'].iloc[news_indices]

# ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„ ๊ธฐ๋ฐ˜ ๋ฐ์ดํ„ฐ ํ”„๋ ˆ์ž„
sim_df = pd.DataFrame(cosine_sim); sim_df

์ดํ›„ dataframe์„ ์œ ์‚ฌ๋„๊ฐ€ 0.5๋ฅผ ๋„˜๋Š”์ง€์— ๋Œ€ํ•œ ์œ ๋ฌด๋กœ boolean index๋กœ ๋งŒ๋“ค์–ด ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„์œผ๋กœ ๋งŒ๋“ค์—ˆ๋‹ค.

# Boolean Index ๋งŒ๋“ค๊ธฐ
sim_boolean = sim_df > 0.5 ; sim_boolean

# ์œ ์‚ฌ๋„ 0.5๊ฐ€ ๋„˜๋Š” ๊ฒŒ์‹œ๊ธ€์ด 10๊ฐœ ์ด์ƒ ์žˆ๋Š” ๊ฒฝ์šฐ
sim_boolean.sum() >= 10

# 10๊ฐœ ์ด์ƒ ์žˆ๋Š” ๊ฒŒ์‹œ๊ธ€์ด ๋ช‡ ๊ฐœ๋‚˜ ์žˆ์„๊นŒ?
sum(sim_boolean.sum() >= 10) #๊ฒฐ๊ณผ: 2465

4. ๊ตฐ์ง‘ํ™”

์ตœ์ข…์ ์œผ๋กœ, ์œ ์‚ฌ๋„๊ฐ€ 25% ์ด์ƒ์ธ ๊ฒŒ์‹œ๋ฌผ๋“ค๋กœ ๊ตฐ์ง‘ํ™”๋ฅผ ์ง„ํ–‰ํ•ด ๋ณด์•˜๋‹ค.

# ์œ ์‚ฌ๋„ 25% ์ด์ƒ์˜ ๋‰ด์Šค ํ† ํ”ฝ ๋ฌถ๊ธฐ

idx_list = list(sim_df[sim_boolean.sum() >= 10].index)
cluster = []
pass_list = []
threshold = 0.25
id_idx = []
for i in idx_list:
    if i not in pass_list:
        idx = sim_df[i][sim_df[i] > threshold].index
        cluster.append(idx)
        pass_list.extend(list(idx))
        id_idx.append(i)
    else:
        pass

print(len(cluster)) #๊ฒฐ๊ณผ: 106
# ์Šคํƒ€๋ฒ…์Šค ๋‰ด์Šค ํ† ํ”ฝ
starbucks_df = df.iloc[id_idx,:].copy(); starbucks_df

์ „๋ณด๋‹ค ํ›จ์”ฌ '์Šคํƒ€๋ฒ…์Šค'์™€ '์ปคํ”ผ' ๊ด€๋ จ๋œ ๊ธฐ์‚ฌ๋“ค๋กœ ๋ฌถ์ธ ๊ฒƒ ๊ฐ™์•„ ์„ฑ๊ณต์ ์œผ๋กœ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ–ˆ๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค.


์ด๋ ‡๊ฒŒ, ์Šคํƒ€๋ฒ…์Šค ๊ธฐ์‚ฌ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ TF-IDF๋ฅผ ์ ์šฉํ•˜๊ณ , ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„๋ฅผ ๊ตฌํ•˜์—ฌ ์œ ์‚ฌํ•œ ๊ธฐ์‚ฌ๋“ค๋ผ๋ฆฌ์˜ ๊ตฐ์ง‘ํ™”๋ฅผ ์ง„ํ–‰ํ•ด ๋ณด์•˜๋‹ค.

 

'๐Ÿ“š ์Šคํ„ฐ๋”” > NLP' ์นดํ…Œ๊ณ ๋ฆฌ์˜ ๋‹ค๋ฅธ ๊ธ€

[Word2Vec์— ๊ด€ํ•˜์—ฌ] - CBOW, Skip-gram  (0) 2023.07.25
[NLP-์Šคํ„ฐ๋””] RNN์— ๊ด€ํ•˜์—ฌ  (1) 2023.06.29
[ํ…์ŠคํŠธ๋งˆ์ด๋‹] 2-1. ํ…์ŠคํŠธ ํ‘œํ˜„๊ณผ ๋ฌธ์„œ ์œ ์‚ฌ๋„  (0) 2023.06.19
[ํ…์ŠคํŠธ๋งˆ์ด๋‹] 1. ํ…์ŠคํŠธ ๋ถ„์„  (0) 2023.06.17
  1. 1. ๋ฐ์ดํ„ฐ ํ™•์ธ ๋ฐ ์ „์ฒ˜๋ฆฌ
  2. 2. TF-IDF ์ ์šฉ
  3. 3. ์ฝ”์‚ฌ์ธ ์œ ์‚ฌ๋„ ๊ตฌํ•˜๊ธฐ
  4. 4. ๊ตฐ์ง‘ํ™”
'๐Ÿ“š ์Šคํ„ฐ๋””/NLP' ์นดํ…Œ๊ณ ๋ฆฌ์˜ ๋‹ค๋ฅธ ๊ธ€
  • [Word2Vec์— ๊ด€ํ•˜์—ฌ] - CBOW, Skip-gram
  • [NLP-์Šคํ„ฐ๋””] RNN์— ๊ด€ํ•˜์—ฌ
  • [ํ…์ŠคํŠธ๋งˆ์ด๋‹] 2-1. ํ…์ŠคํŠธ ํ‘œํ˜„๊ณผ ๋ฌธ์„œ ์œ ์‚ฌ๋„
  • [ํ…์ŠคํŠธ๋งˆ์ด๋‹] 1. ํ…์ŠคํŠธ ๋ถ„์„
์žฅ์˜์ค€
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groomielife
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youngjangjoon
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์ „์ฒด
์˜ค๋Š˜
์–ด์ œ
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      • ๐ŸŽ“ RESUMAI (6)
      • ๐Ÿงธ TOY-PROJECTS (8)
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์ธ๊ธฐ ๊ธ€

ํƒœ๊ทธ

  • ArcFace
  • allauth
  • DEEPALIGNED
  • vectordb
  • MTP-CL
  • text embedding
  • text clustering
  • NeuralNet
  • ์ž์†Œ์„œ์ƒ์„ฑํ”„๋กœ์ ํŠธ
  • DEEPLOOK
  • contrastive learning
  • Representation Training
  • CS224N
  • ์ƒ์„ฑAI
  • pinecone
  • RESUMAI
  • gpt-1
  • Haar-cascade
  • dj-rest-auth
  • ๋น„๋™๊ธฐ ์ €์žฅ
  • Neural Net
  • Conversational Agent
  • GenAI
  • project
  • ์ž๊ธฐ์†Œ๊ฐœ์„œ์ƒ์„ฑ
  • cv
  • NLP
  • story discovery
  • rag
  • ๋…ผ๋ฌธ
hELLO ยท Designed By ์ •์ƒ์šฐ.
์žฅ์˜์ค€
[ํ…์ŠคํŠธ๋งˆ์ด๋‹] 2-2. ๋ฌธ์„œ ์œ ์‚ฌ๋„ - ์Šคํƒ€๋ฒ…์Šค ๋„ค์ด๋ฒ„ ๋ฌธ์„œ ์œ ์‚ฌ๋„ ๊ตฌํ•˜๊ธฐ
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* ๋‹จ์ถ•ํ‚ค๋Š” ํ•œ๊ธ€/์˜๋ฌธ ๋Œ€์†Œ๋ฌธ์ž๋กœ ์ด์šฉ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ํ‹ฐ์Šคํ† ๋ฆฌ ๊ธฐ๋ณธ ๋„๋ฉ”์ธ์—์„œ๋งŒ ๋™์ž‘ํ•ฉ๋‹ˆ๋‹ค.