1 | Korea | 7.14% | |
2 | US | 7.14% | |
3 | N. | 5.36% | |
4 | China | 3.57% | |
5 | Coach | 1.79% | |
6 | Hong | 1.79% | |
7 | US’s | 1.79% | |
8 | Wins | 1.79% | |
9 | Punishment | 1.79% | |
10 | Russia | 1.79% | |
11 | [What | 1.79% | |
12 | Cheongdam-dong | 1.79% | |
13 | Refresh | 1.79% | |
14 | S. | 1.79% | |
15 | Fans | 1.79% | |
16 | Relations | 1.79% | |
17 | Tesla | 1.79% | |
18 | An | 1.79% | |
19 | Baceprot | 1.79% | |
20 | Panchen | 1.79% | |
21 | See] | 1.79% | |
22 | Girl | 1.79% | |
23 | Bubble | 1.79% | |
24 | Kong | 1.79% | |
25 | Gum | 1.79% | |
26 | Power | 1.79% | |
27 | [Weekender] | 1.79% | |
28 | Seoul | 1.79% | |
29 | Does | 1.79% | |
30 | [Music | 1.79% | |
31 | EV | 1.79% | |
32 | DDP | 1.79% | |
33 | drama] | 1.79% | |
34 | Blinken | 1.79% | |
35 | Roundup | 1.79% | |
36 | How | 1.79% | |
37 | Kinmen | 1.79% | |
38 | [EYE] | 1.79% | |
39 | Steel | 1.79% | |
40 | Lama | 1.79% | |
41 | Voice | 1.79% | |
42 | Chinese | 1.79% | |
43 | ‘Save | 1.79% | |
44 | Tibet’s | 1.79% | |
45 | Songs | 1.79% | |
46 | Taiwan | 1.79% | |
47 | Act | 1.79% | |
는 분류, 데이터 기준으로 의 기사에서 의 고유명사 데이터를 통해 생성되었습니다.
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