Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Multilinguality:
multilingual
Size Categories:
n<1K
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 25,947 Bytes
86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 6755c8a 1d6aeb3 86083d6 6755c8a 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 1d6aeb3 86083d6 b5ea44f 86083d6 1d6aeb3 86083d6 26961a2 b5ea44f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 |
---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
languages:
ace:
- ace
af:
- af
als:
- als
am:
- am
an:
- an
ang:
- ang
ar:
- ar
arc:
- arc
arz:
- arz
as:
- as
ast:
- ast
ay:
- ay
az:
- az
ba:
- ba
bar:
- bar
be:
- be
bg:
- bg
bh:
- bh
bn:
- bn
bo:
- bo
br:
- br
bs:
- bs
ca:
- ca
cdo:
- cdo
ce:
- ce
ceb:
- ceb
ckb:
- ckb
co:
- co
crh:
- crh
cs:
- cs
csb:
- csb
cv:
- cv
cy:
- cy
da:
- da
de:
- de
diq:
- diq
dv:
- dv
el:
- el
en:
- en
eo:
- eo
es:
- es
et:
- et
eu:
- eu
ext:
- ext
fa:
- fa
fi:
- fi
fo:
- fo
fr:
- fr
frr:
- frr
fur:
- fur
fy:
- fy
ga:
- ga
gan:
- gan
gd:
- gd
gl:
- gl
gn:
- gn
gu:
- gu
hak:
- hak
he:
- he
hi:
- hi
hr:
- hr
hsb:
- hsb
hu:
- hu
hy:
- hy
ia:
- ia
id:
- id
ig:
- ig
ilo:
- ilo
io:
- io
is:
- is
it:
- it
ja:
- ja
jbo:
- jbo
jv:
- jv
ka:
- ka
kk:
- kk
km:
- km
kn:
- kn
ko:
- ko
ksh:
- ksh
ku:
- ku
ky:
- ky
la:
- la
lb:
- lb
li:
- li
lij:
- lij
lmo:
- lmo
ln:
- ln
lt:
- lt
lv:
- lv
mg:
- mg
mhr:
- mhr
mi:
- mi
min:
- min
mk:
- mk
ml:
- ml
mn:
- mn
mr:
- mr
ms:
- ms
mt:
- mt
mwl:
- mwl
my:
- my
mzn:
- mzn
nap:
- nap
nds:
- nds
ne:
- ne
nl:
- nl
nn:
- nn
'no':
- 'no'
nov:
- nov
oc:
- oc
or:
- or
os:
- os
other-bat-smg:
- sgs
other-be-x-old:
- be-tarask
other-cbk-zam:
- cbk
other-eml:
- eml
other-fiu-vro:
- vro
other-map-bms:
- jv-x-bms
other-simple:
- en-basiceng
other-zh-classical:
- lzh
other-zh-min-nan:
- nan
other-zh-yue:
- yue
pa:
- pa
pdc:
- pdc
pl:
- pl
pms:
- pms
pnb:
- pnb
ps:
- ps
pt:
- pt
qu:
- qu
rm:
- rm
ro:
- ro
ru:
- ru
rw:
- rw
sa:
- sa
sah:
- sah
scn:
- scn
sco:
- sco
sd:
- sd
sh:
- sh
si:
- si
sk:
- sk
sl:
- sl
so:
- so
sq:
- sq
sr:
- sr
su:
- su
sv:
- sv
sw:
- sw
szl:
- szl
ta:
- ta
te:
- te
tg:
- tg
th:
- th
tk:
- tk
tl:
- tl
tr:
- tr
tt:
- tt
ug:
- ug
uk:
- uk
ur:
- ur
uz:
- uz
vec:
- vec
vep:
- vep
vi:
- vi
vls:
- vls
vo:
- vo
wa:
- wa
war:
- war
wuu:
- wuu
xmf:
- xmf
yi:
- yi
yo:
- yo
zea:
- zea
zh:
- zh
licenses:
- unknown
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
paperswithcode_id: wikiann-1
pretty_name: WikiANN
---
# Dataset Card for WikiANN
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner)
- **Repository:** [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner)
- **Paper:** The original datasets come from the _Cross-lingual name tagging and linking for 282 languages_ [paper](https://www.aclweb.org/anthology/P17-1178/) by Xiaoman Pan et al. (2018). This version corresponds to the balanced train, dev, and test splits of the original data from the _Massively Multilingual Transfer for NER_ [paper](https://arxiv.org/abs/1902.00193) by Afshin Rahimi et al. (2019).
- **Leaderboard:**
- **Point of Contact:** [Afshin Rahimi](mailto:afshinrahimi@gmail.com) or [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com) or [Albert Villanova del Moral](albert@huggingface.co)
### Dataset Summary
WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus.
### Supported Tasks and Leaderboards
- `named-entity-recognition`: The dataset can be used to train a model for named entity recognition in many languages, or evaluate the zero-shot cross-lingual capabilities of multilingual models.
### Languages
The dataset contains 176 languages, one in each of the configuration subsets. The corresponding BCP 47 language tags
are:
| | Language tag |
|:-------------------|:---------------|
| ace | ace |
| af | af |
| als | als |
| am | am |
| an | an |
| ang | ang |
| ar | ar |
| arc | arc |
| arz | arz |
| as | as |
| ast | ast |
| ay | ay |
| az | az |
| ba | ba |
| bar | bar |
| be | be |
| bg | bg |
| bh | bh |
| bn | bn |
| bo | bo |
| br | br |
| bs | bs |
| ca | ca |
| cdo | cdo |
| ce | ce |
| ceb | ceb |
| ckb | ckb |
| co | co |
| crh | crh |
| cs | cs |
| csb | csb |
| cv | cv |
| cy | cy |
| da | da |
| de | de |
| diq | diq |
| dv | dv |
| el | el |
| en | en |
| eo | eo |
| es | es |
| et | et |
| eu | eu |
| ext | ext |
| fa | fa |
| fi | fi |
| fo | fo |
| fr | fr |
| frr | frr |
| fur | fur |
| fy | fy |
| ga | ga |
| gan | gan |
| gd | gd |
| gl | gl |
| gn | gn |
| gu | gu |
| hak | hak |
| he | he |
| hi | hi |
| hr | hr |
| hsb | hsb |
| hu | hu |
| hy | hy |
| ia | ia |
| id | id |
| ig | ig |
| ilo | ilo |
| io | io |
| is | is |
| it | it |
| ja | ja |
| jbo | jbo |
| jv | jv |
| ka | ka |
| kk | kk |
| km | km |
| kn | kn |
| ko | ko |
| ksh | ksh |
| ku | ku |
| ky | ky |
| la | la |
| lb | lb |
| li | li |
| lij | lij |
| lmo | lmo |
| ln | ln |
| lt | lt |
| lv | lv |
| mg | mg |
| mhr | mhr |
| mi | mi |
| min | min |
| mk | mk |
| ml | ml |
| mn | mn |
| mr | mr |
| ms | ms |
| mt | mt |
| mwl | mwl |
| my | my |
| mzn | mzn |
| nap | nap |
| nds | nds |
| ne | ne |
| nl | nl |
| nn | nn |
| no | no |
| nov | nov |
| oc | oc |
| or | or |
| os | os |
| other-bat-smg | sgs |
| other-be-x-old | be-tarask |
| other-cbk-zam | cbk |
| other-eml | eml |
| other-fiu-vro | vro |
| other-map-bms | jv-x-bms |
| other-simple | en-basiceng |
| other-zh-classical | lzh |
| other-zh-min-nan | nan |
| other-zh-yue | yue |
| pa | pa |
| pdc | pdc |
| pl | pl |
| pms | pms |
| pnb | pnb |
| ps | ps |
| pt | pt |
| qu | qu |
| rm | rm |
| ro | ro |
| ru | ru |
| rw | rw |
| sa | sa |
| sah | sah |
| scn | scn |
| sco | sco |
| sd | sd |
| sh | sh |
| si | si |
| sk | sk |
| sl | sl |
| so | so |
| sq | sq |
| sr | sr |
| su | su |
| sv | sv |
| sw | sw |
| szl | szl |
| ta | ta |
| te | te |
| tg | tg |
| th | th |
| tk | tk |
| tl | tl |
| tr | tr |
| tt | tt |
| ug | ug |
| uk | uk |
| ur | ur |
| uz | uz |
| vec | vec |
| vep | vep |
| vi | vi |
| vls | vls |
| vo | vo |
| wa | wa |
| war | war |
| wuu | wuu |
| xmf | xmf |
| yi | yi |
| yo | yo |
| zea | zea |
| zh | zh |
## Dataset Structure
### Data Instances
This is an example in the "train" split of the "af" (Afrikaans language) configuration subset:
```python
{
'tokens': ['Sy', 'ander', 'seun', ',', 'Swjatopolk', ',', 'was', 'die', 'resultaat', 'van', '’n', 'buite-egtelike', 'verhouding', '.'],
'ner_tags': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'langs': ['af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af'],
'spans': ['PER: Swjatopolk']
}
```
### Data Fields
- `tokens`: a `list` of `string` features.
- `langs`: a `list` of `string` features that correspond to the language of each token.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-PER` (1), `I-PER` (2), `B-ORG` (3), `I-ORG` (4), `B-LOC` (5), `I-LOC` (6).
- `spans`: a `list` of `string` features, that is the list of named entities in the input text formatted as ``<TAG>: <mention>``
### Data Splits
For each configuration subset, the data is split into "train", "validation" and "test" sets, each containing the
following number of examples:
| | Train | Validation | Test |
|:-------------|--------:|-------------:|-------:|
| ace | 100 | 100 | 100 |
| af | 5000 | 1000 | 1000 |
| als | 100 | 100 | 100 |
| am | 100 | 100 | 100 |
| an | 1000 | 1000 | 1000 |
| ang | 100 | 100 | 100 |
| ar | 20000 | 10000 | 10000 |
| arc | 100 | 100 | 100 |
| arz | 100 | 100 | 100 |
| as | 100 | 100 | 100 |
| ast | 1000 | 1000 | 1000 |
| ay | 100 | 100 | 100 |
| az | 10000 | 1000 | 1000 |
| ba | 100 | 100 | 100 |
| bar | 100 | 100 | 100 |
| bat-smg | 100 | 100 | 100 |
| be | 15000 | 1000 | 1000 |
| be-x-old | 5000 | 1000 | 1000 |
| bg | 20000 | 10000 | 10000 |
| bh | 100 | 100 | 100 |
| bn | 10000 | 1000 | 1000 |
| bo | 100 | 100 | 100 |
| br | 1000 | 1000 | 1000 |
| bs | 15000 | 1000 | 1000 |
| ca | 20000 | 10000 | 10000 |
| cbk-zam | 100 | 100 | 100 |
| cdo | 100 | 100 | 100 |
| ce | 100 | 100 | 100 |
| ceb | 100 | 100 | 100 |
| ckb | 1000 | 1000 | 1000 |
| co | 100 | 100 | 100 |
| crh | 100 | 100 | 100 |
| cs | 20000 | 10000 | 10000 |
| csb | 100 | 100 | 100 |
| cv | 100 | 100 | 100 |
| cy | 10000 | 1000 | 1000 |
| da | 20000 | 10000 | 10000 |
| de | 20000 | 10000 | 10000 |
| diq | 100 | 100 | 100 |
| dv | 100 | 100 | 100 |
| el | 20000 | 10000 | 10000 |
| eml | 100 | 100 | 100 |
| en | 20000 | 10000 | 10000 |
| eo | 15000 | 10000 | 10000 |
| es | 20000 | 10000 | 10000 |
| et | 15000 | 10000 | 10000 |
| eu | 10000 | 10000 | 10000 |
| ext | 100 | 100 | 100 |
| fa | 20000 | 10000 | 10000 |
| fi | 20000 | 10000 | 10000 |
| fiu-vro | 100 | 100 | 100 |
| fo | 100 | 100 | 100 |
| fr | 20000 | 10000 | 10000 |
| frr | 100 | 100 | 100 |
| fur | 100 | 100 | 100 |
| fy | 1000 | 1000 | 1000 |
| ga | 1000 | 1000 | 1000 |
| gan | 100 | 100 | 100 |
| gd | 100 | 100 | 100 |
| gl | 15000 | 10000 | 10000 |
| gn | 100 | 100 | 100 |
| gu | 100 | 100 | 100 |
| hak | 100 | 100 | 100 |
| he | 20000 | 10000 | 10000 |
| hi | 5000 | 1000 | 1000 |
| hr | 20000 | 10000 | 10000 |
| hsb | 100 | 100 | 100 |
| hu | 20000 | 10000 | 10000 |
| hy | 15000 | 1000 | 1000 |
| ia | 100 | 100 | 100 |
| id | 20000 | 10000 | 10000 |
| ig | 100 | 100 | 100 |
| ilo | 100 | 100 | 100 |
| io | 100 | 100 | 100 |
| is | 1000 | 1000 | 1000 |
| it | 20000 | 10000 | 10000 |
| ja | 20000 | 10000 | 10000 |
| jbo | 100 | 100 | 100 |
| jv | 100 | 100 | 100 |
| ka | 10000 | 10000 | 10000 |
| kk | 1000 | 1000 | 1000 |
| km | 100 | 100 | 100 |
| kn | 100 | 100 | 100 |
| ko | 20000 | 10000 | 10000 |
| ksh | 100 | 100 | 100 |
| ku | 100 | 100 | 100 |
| ky | 100 | 100 | 100 |
| la | 5000 | 1000 | 1000 |
| lb | 5000 | 1000 | 1000 |
| li | 100 | 100 | 100 |
| lij | 100 | 100 | 100 |
| lmo | 100 | 100 | 100 |
| ln | 100 | 100 | 100 |
| lt | 10000 | 10000 | 10000 |
| lv | 10000 | 10000 | 10000 |
| map-bms | 100 | 100 | 100 |
| mg | 100 | 100 | 100 |
| mhr | 100 | 100 | 100 |
| mi | 100 | 100 | 100 |
| min | 100 | 100 | 100 |
| mk | 10000 | 1000 | 1000 |
| ml | 10000 | 1000 | 1000 |
| mn | 100 | 100 | 100 |
| mr | 5000 | 1000 | 1000 |
| ms | 20000 | 1000 | 1000 |
| mt | 100 | 100 | 100 |
| mwl | 100 | 100 | 100 |
| my | 100 | 100 | 100 |
| mzn | 100 | 100 | 100 |
| nap | 100 | 100 | 100 |
| nds | 100 | 100 | 100 |
| ne | 100 | 100 | 100 |
| nl | 20000 | 10000 | 10000 |
| nn | 20000 | 1000 | 1000 |
| no | 20000 | 10000 | 10000 |
| nov | 100 | 100 | 100 |
| oc | 100 | 100 | 100 |
| or | 100 | 100 | 100 |
| os | 100 | 100 | 100 |
| pa | 100 | 100 | 100 |
| pdc | 100 | 100 | 100 |
| pl | 20000 | 10000 | 10000 |
| pms | 100 | 100 | 100 |
| pnb | 100 | 100 | 100 |
| ps | 100 | 100 | 100 |
| pt | 20000 | 10000 | 10000 |
| qu | 100 | 100 | 100 |
| rm | 100 | 100 | 100 |
| ro | 20000 | 10000 | 10000 |
| ru | 20000 | 10000 | 10000 |
| rw | 100 | 100 | 100 |
| sa | 100 | 100 | 100 |
| sah | 100 | 100 | 100 |
| scn | 100 | 100 | 100 |
| sco | 100 | 100 | 100 |
| sd | 100 | 100 | 100 |
| sh | 20000 | 10000 | 10000 |
| si | 100 | 100 | 100 |
| simple | 20000 | 1000 | 1000 |
| sk | 20000 | 10000 | 10000 |
| sl | 15000 | 10000 | 10000 |
| so | 100 | 100 | 100 |
| sq | 5000 | 1000 | 1000 |
| sr | 20000 | 10000 | 10000 |
| su | 100 | 100 | 100 |
| sv | 20000 | 10000 | 10000 |
| sw | 1000 | 1000 | 1000 |
| szl | 100 | 100 | 100 |
| ta | 15000 | 1000 | 1000 |
| te | 1000 | 1000 | 1000 |
| tg | 100 | 100 | 100 |
| th | 20000 | 10000 | 10000 |
| tk | 100 | 100 | 100 |
| tl | 10000 | 1000 | 1000 |
| tr | 20000 | 10000 | 10000 |
| tt | 1000 | 1000 | 1000 |
| ug | 100 | 100 | 100 |
| uk | 20000 | 10000 | 10000 |
| ur | 20000 | 1000 | 1000 |
| uz | 1000 | 1000 | 1000 |
| vec | 100 | 100 | 100 |
| vep | 100 | 100 | 100 |
| vi | 20000 | 10000 | 10000 |
| vls | 100 | 100 | 100 |
| vo | 100 | 100 | 100 |
| wa | 100 | 100 | 100 |
| war | 100 | 100 | 100 |
| wuu | 100 | 100 | 100 |
| xmf | 100 | 100 | 100 |
| yi | 100 | 100 | 100 |
| yo | 100 | 100 | 100 |
| zea | 100 | 100 | 100 |
| zh | 20000 | 10000 | 10000 |
| zh-classical | 100 | 100 | 100 |
| zh-min-nan | 100 | 100 | 100 |
| zh-yue | 20000 | 10000 | 10000 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
The original 282 datasets are associated with this article
```
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
abstract = "The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia. Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable. We achieve this goal by performing a series of new KB mining methods: generating {``}silver-standard{''} annotations by transferring annotations from English to other languages through cross-lingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from cross-lingual links. Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.",
}
```
while the 176 languages supported in this version are associated with the following article
```
@inproceedings{rahimi-etal-2019-massively,
title = "Massively Multilingual Transfer for {NER}",
author = "Rahimi, Afshin and
Li, Yuan and
Cohn, Trevor",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1015",
pages = "151--164",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) and [@rabeehk](https://github.com/rabeehk) for adding this dataset.
|