1 | Australia | 2.7% | |
2 | Queenstown | 2.7% | |
3 | Canada | 1.8% | |
4 | NATO | 1.8% | |
5 | State | 1.8% | |
6 | Plimmer | 0.9% | |
7 | Marlene | 0.9% | |
8 | Kyiv | 0.9% | |
9 | Up | 0.9% | |
10 | NT | 0.9% | |
11 | Hoofprints | 0.9% | |
12 | Xi’s | 0.9% | |
13 | Number | 0.9% | |
14 | Rishi | 0.9% | |
15 | Gorgeous | 0.9% | |
16 | Bali | 0.9% | |
17 | Gastro | 0.9% | |
18 | Cooma | 0.9% | |
19 | Bank | 0.9% | |
20 | ‘We | 0.9% | |
21 | Pakistans | 0.9% | |
22 | Obnoxious | 0.9% | |
23 | Navy | 0.9% | |
24 | Fed | 0.9% | |
25 | Picks | 0.9% | |
26 | Venezuela | 0.9% | |
27 | Thomas | 0.9% | |
28 | Woman | 0.9% | |
29 | HEY | 0.9% | |
30 | Pile | 0.9% | |
31 | F-35 | 0.9% | |
32 | Russia | 0.9% | |
33 | Marketplace | 0.9% | |
34 | Islands | 0.9% | |
35 | New | 0.9% | |
36 | Brazil | 0.9% | |
37 | Chris | 0.9% | |
38 | Beach | 0.9% | |
39 | Die | 0.9% | |
40 | Kaba | 0.9% | |
41 | NYPD | 0.9% | |
42 | Jong | 0.9% | |
43 | Ballarat | 0.9% | |
44 | Indonesia | 0.9% | |
45 | Emergency | 0.9% | |
46 | Yard | 0.9% | |
47 | GP | 0.9% | |
48 | Bevern | 0.9% | |
49 | Ukrainian | 0.9% | |
50 | Submarine | 0.9% | |
는 분류, 데이터 기준으로 의 기사에서 의 고유명사 데이터를 통해 생성되었습니다.
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