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Nationality Prediction from Name

Project description

name2nat: a Python package for nationality prediction from a name

name2nat is a Python package that predicts the nationality of any name written in Roman letters. For example, it returns the correct output Korean for my name `Kyubyong Park'. Needless to say, it is not possible to guess somebody's nationality 100% right from their name. After all, nationality can change, you know. However, it is also true that there is a tendency between names and nationality. So it turns out statistical classifiers for this task works to some extent. Details are explained below.

NaNa Dataset

Construction

I constructed a new dataset for this project because I failed to find any available dataset that is big and comprehensive enough.

  • STEP 1. Downloaded and extracted the 20200601 English wiki dump (enwiki-20200601-pages-articles.xml).
  • STEP 2. Iterated all pages and collected the title and the nationality. I regarded the title as a person if the Category section at the bottom of each page included ... births (green rectangule), and identified their nationality from the most frequent nationality word in the section (red rectangules).
* STEP 3. Randomly split the data into train/dev/test in the ratio of 8:1:1 within each nationality group.

Stats

Nationality Train Dev Test
Total 1112902 890248 111286 111368
Afghan 778 97 98
Albanian 2193 274 275
Algerian 1592 199 200
American 241772 30221 30222
Andorran 188 24 24
Angolan 504 63 63
Argentine 8926 1116 1116
Armenian 1600 200 201
Aruban 93 12 12
Australian 40536 5067 5067
Austrian 9192 1149 1149
Azerbaijani 1331 166 167
Bahamian 233 29 30
Bahraini 237 30 30
Bangladeshi 1636 204 205
Barbadian 372 47 47
Basque 961 120 121
Belarusian 2338 292 293
Belgian 7907 988 989
Belizean 148 19 19
Beninese 199 25 25
Bermudian 270 34 34
Bhutanese 144 18 18
Bolivian 657 82 83
Bosniak 81 10 11
Botswana 252 31 32
Brazilian 11234 1404 1405
Breton 118 15 15
British 45922 5740 5741
Bruneian 115 14 15
Bulgarian 3926 491 491
Burkinabé 289 36 37
Burmese 944 118 118
Burundian 140 17 18
Cambodian 360 45 46
Cameroonian 1028 129 129
Canadian 34152 4269 4270
Catalan 1717 215 215
Chadian 139 17 18
Chilean 2838 355 355
Chinese 9494 1187 1187
Colombian 2620 328 328
Comorian 54 7 7
Congolese 35 4 5
Cuban 1938 242 243
Cypriot 1016 127 128
Czech 7244 906 906
Dane 32 4 5
Djiboutian 54 7 7
Dominican 1580 198 198
Dutch 14916 1864 1865
Ecuadorian 874 109 110
Egyptian 2776 347 348
Emirati 621 78 78
English 77159 9645 9645
Equatoguinean 193 24 25
Eritrean 133 17 17
Estonian 2028 254 254
Ethiopian 733 92 92
Faroese 284 35 36
Filipino 3928 491 491
Finn 68 8 9
French 40841 5105 5106
Gabonese 180 23 23
Gambian 220 28 28
Georgian 262 33 33
German 42388 5299 5299
Ghanaian 2036 255 255
Gibraltarian 98 12 13
Greek 5975 747 747
Grenadian 139 17 18
Guatemalan 563 70 71
Guinean 584 73 74
Guyanese 358 45 45
Haitian 561 70 71
Honduran 500 63 63
Hungarian 7220 903 903
I-Kiribati 40 5 6
Indian 22692 2836 2837
Indonesian 2820 352 353
Iranian 5010 626 627
Iraqi 1252 157 157
Irish 11844 1481 1481
Israeli 5149 644 644
Italian 29336 3667 3668
Jamaican 1422 178 178
Japanese 21216 2652 2652
Jordanian 490 61 62
Kazakh 24 3 4
Kenyan 1609 201 202
Korean 7896 987 988
Kuwaiti 396 50 50
Kyrgyz 16 2 2
Lao 26 3 4
Latvian 1693 212 212
Lebanese 1246 156 156
Liberian 294 37 37
Libyan 271 34 34
Lithuanian 1979 247 248
Macedonian 1099 137 138
Malagasy 232 29 29
Malawian 219 27 28
Malaysian 2582 323 323
Maldivian 152 19 20
Malian 385 48 49
Maltese 663 83 83
Manx 150 19 19
Marshallese 32 4 4
Mauritanian 96 12 12
Mauritian 263 33 33
Mexican 8648 1081 1081
Moldovan 1000 125 125
Mongolian 504 63 64
Montenegrin 955 119 120
Moroccan 1457 182 183
Mozambican 210 26 27
Namibian 588 74 74
Nauruan 32 4 4
Nepalese 773 97 97
Nicaraguan 285 36 36
Nigerian 4060 507 508
Nigerien 143 18 18
Norwegian 13512 1689 1690
Omani 197 25 25
Pakistani 3762 470 471
Palauan 35 4 5
Palestinian 528 66 66
Panamanian 474 59 60
Paraguayan 1012 127 127
Peruvian 1521 190 191
Portuguese 4734 592 592
Qatari 548 68 69
Romanian 6551 819 819
Russian 21274 2659 2660
Rwandan 269 34 34
Salvadoran 507 63 64
Sammarinese 198 25 25
Samoan 596 75 75
Saudi 1496 187 188
Senegalese 823 103 103
Serb 44 6 6
Singaporean 1316 165 165
Slovak 2867 358 359
Slovene 88 11 12
Somali 116 14 15
Sotho 49 6 7
Sudanese 348 44 44
Surinamese 200 25 25
Swazi 114 14 15
Syriac 78 10 10
Syrian 1047 131 131
Taiwanese 1946 243 244
Tajik 61 8 8
Tamil 1399 175 175
Tanzanian 627 78 79
Thai 2747 343 344
Tibetan 265 33 34
Togolese 211 26 27
Tongan 456 57 57
Tunisian 1072 134 134
Turk 79 10 10
Tuvaluan 66 8 9
Ugandan 1052 132 132
Ukrainian 6198 775 775
Uruguayan 2267 283 284
Uzbek 62 8 8
Vanuatuan 116 15 15
Venezuelan 1937 242 243
Vietnamese 1257 157 158
Vincentian 8 1 1
Welsh 5270 659 659
Yemeni 322 40 41
Zambian 510 64 64

Downloadable Link

  • You can download the dataset here.

name2nat

Installation

pip install name2nat

Usage

>>> from name2nat import Name2nat

>>> my_nanat = Name2nat()

>>> names = ["Donald Trump", # American
         "Moon Jae-in", # Korean
         "Shinzo Abe", # Japanese
         "Xi Jinping", # Chinese
         "Joko Widodo", # Indonesian
         "Angela Merkel", # German
         "Emmanuel Macron", # French
         "Kyubyong Park", # Korean
         "Yamamoto Yu", # Japanese
         "Jing Xu"] # Chinese
>>> result = my_nanat(names, top_n=3)
>>> print(result)
# (name, [(nationality, prob), ...])
# Note that prob of 1.0 indicates the name exists
# in Wikipedia.
[
('Donald Trump', [('American', 1.0)])
('Moon Jae-in', [('Korean', 1.0)])
('Shinzo Abe', [('Japanese', 1.0)])
('Xi Jinping', [('Chinese', 1.0)])
('Joko Widodo', [('Indonesian', 1.0)])
('Angela Merkel', [('German', 1.0)])
('Emmanuel Macron', [('French', 1.0)])
('Kyubyong Park', [('Korean', 0.9985014200210571), ('American', 0.000289416522718966), ('Bhutanese', 0.00025851925602182746)])
('Yamamoto Yu', [('Japanese', 0.7050493359565735), ('Taiwanese', 0.12779785692691803), ('Chinese', 0.04263153299689293)])
('Jing Xu', [('Chinese', 0.8626819252967834), ('Taiwanese', 0.09901007264852524), ('American', 0.022995812818408012)])
]

Training

I use a powerful NLP library Flair to train a text classifier model. A bidirectional GRU layer is employed.

python train.py

Evaluation

python predict.py;
python eval.py --gt nana/test.tgt --pred test.pred

Results

K Precision@K
1 61310/111368=55.1
2 77480/111368=69.6
3 86703/111368=77.9
4 92491/111368=83.0
5 96697/111368=86.8

References

If you use this code for research, please cite:

@misc{park2018name2nat,
  author = {Park, Kyubyong},
  title = {name2nat: a Python package for nationality prediction from a name},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Kyubyong/name2nat}}
}

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