1 | Korea | 3.57% | |
2 | Seoul | 3.57% | |
3 | Paris | 3.57% | |
4 | Baguette | 1.79% | |
5 | Museum’s | 1.79% | |
6 | Samsung | 1.79% | |
7 | [KH | 1.79% | |
8 | Zico | 1.79% | |
9 | Motor | 1.79% | |
10 | Se-hwa | 1.79% | |
11 | National | 1.79% | |
12 | River | 1.79% | |
13 | Hyundais | 1.79% | |
14 | Jennie | 1.79% | |
15 | K-pop] | 1.79% | |
16 | Business | 1.79% | |
17 | Im | 1.79% | |
18 | Chan-wook | 1.79% | |
19 | Toray | 1.79% | |
20 | Han | 1.79% | |
21 | IQ’ | 1.79% | |
22 | Venice | 1.79% | |
23 | CEO | 1.79% | |
24 | Olive | 1.79% | |
25 | SNS | 1.79% | |
26 | CJ | 1.79% | |
27 | Sympathizer’ | 1.79% | |
28 | Driver | 1.79% | |
29 | Hyundai | 1.79% | |
30 | North | 1.79% | |
31 | [Today’s | 1.79% | |
32 | Hong | 1.79% | |
33 | GDP | 1.79% | |
34 | Agency | 1.79% | |
35 | SME | 1.79% | |
36 | China | 1.79% | |
37 | Young | 1.79% | |
38 | Host’ | 1.79% | |
39 | Milan | 1.79% | |
40 | Philippines | 1.79% | |
41 | Amazon | 1.79% | |
42 | Rolex | 1.79% | |
43 | Hangeul | 1.79% | |
44 | Biennale | 1.79% | |
45 | US | 1.79% | |
46 | Park | 1.79% | |
47 | Taxi | 1.79% | |
48 | Explains] | 1.79% | |
49 | ‘The | 1.79% | |
50 | South | 1.79% | |
는 분류, 데이터 기준으로 의 기사에서 의 고유명사 데이터를 통해 생성되었습니다.
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