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aarontseng/wikiann

Dataset Card for WikiANN 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… See the full description on the dataset page: https://huggingface.co/datasets/aarontseng/wikiann.

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Dataset Card

Dataset Card for WikiANN

Table of Contents

Dataset Description

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
aceace
afaf
alsals
amam
anan
angang
arar
arcarc
arzarz
asas
astast
ayay
azaz
baba
barbar
bebe
bgbg
bhbh
bnbn
bobo
brbr
bsbs
caca
cdocdo
cece
cebceb
ckbckb
coco
crhcrh
cscs
csbcsb
cvcv
cycy
dada
dede
diqdiq
dvdv
elel
enen
eoeo
eses
etet
eueu
extext
fafa
fifi
fofo
frfr
frrfrr
furfur
fyfy
gaga
gangan
gdgd
glgl
gngn
gugu
hakhak
hehe
hihi
hrhr
hsbhsb
huhu
hyhy
iaia
idid
igig
iloilo
ioio
isis
itit
jaja
jbojbo
jvjv
kaka
kkkk
kmkm
knkn
koko
kshksh
kuku
kyky
lala
lblb
lili
lijlij
lmolmo
lnln
ltlt
lvlv
mgmg
mhrmhr
mimi
minmin
mkmk
mlml
mnmn
mrmr
msms
mtmt
mwlmwl
mymy
mznmzn
napnap
ndsnds
nene
nlnl
nnnn
nono
novnov
ococ
oror
osos
other-bat-smgsgs
other-be-x-oldbe-tarask
other-cbk-zamcbk
other-emleml
other-fiu-vrovro
other-map-bmsjv-x-bms
other-simpleen-basiceng
other-zh-classicallzh
other-zh-min-nannan
other-zh-yueyue
papa
pdcpdc
plpl
pmspms
pnbpnb
psps
ptpt
ququ
rmrm
roro
ruru
rwrw
sasa
sahsah
scnscn
scosco
sdsd
shsh
sisi
sksk
slsl
soso
sqsq
srsr
susu
svsv
swsw
szlszl
tata
tete
tgtg
thth
tktk
tltl
trtr
tttt
ugug
ukuk
urur
uzuz
vecvec
vepvep
vivi
vlsvls
vovo
wawa
warwar
wuuwuu
xmfxmf
yiyi
yoyo
zeazea
zhzh

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:

TrainValidationTest
ace100100100
af500010001000
als100100100
am100100100
an100010001000
ang100100100
ar200001000010000
arc100100100
arz100100100
as100100100
ast100010001000
ay100100100
az1000010001000
ba100100100
bar100100100
bat-smg100100100
be1500010001000
be-x-old500010001000
bg200001000010000
bh100100100
bn1000010001000
bo100100100
br100010001000
bs1500010001000
ca200001000010000
cbk-zam100100100
cdo100100100
ce100100100
ceb100100100
ckb100010001000
co100100100
crh100100100
cs200001000010000
csb100100100
cv100100100
cy1000010001000
da200001000010000
de200001000010000
diq100100100
dv100100100
el200001000010000
eml100100100
en200001000010000
eo150001000010000
es200001000010000
et150001000010000
eu100001000010000
ext100100100
fa200001000010000
fi200001000010000
fiu-vro100100100
fo100100100
fr200001000010000
frr100100100
fur100100100
fy100010001000
ga100010001000
gan100100100
gd100100100
gl150001000010000
gn100100100
gu100100100
hak100100100
he200001000010000
hi500010001000
hr200001000010000
hsb100100100
hu200001000010000
hy1500010001000
ia100100100
id200001000010000
ig100100100
ilo100100100
io100100100
is100010001000
it200001000010000
ja200001000010000
jbo100100100
jv100100100
ka100001000010000
kk100010001000
km100100100
kn100100100
ko200001000010000
ksh100100100
ku100100100
ky100100100
la500010001000
lb500010001000
li100100100
lij100100100
lmo100100100
ln100100100
lt100001000010000
lv100001000010000
map-bms100100100
mg100100100
mhr100100100
mi100100100
min100100100
mk1000010001000
ml1000010001000
mn100100100
mr500010001000
ms2000010001000
mt100100100
mwl100100100
my100100100
mzn100100100
nap100100100
nds100100100
ne100100100
nl200001000010000
nn2000010001000
no200001000010000
nov100100100
oc100100100
or100100100
os100100100
pa100100100
pdc100100100
pl200001000010000
pms100100100
pnb100100100
ps100100100
pt200001000010000
qu100100100
rm100100100
ro200001000010000
ru200001000010000
rw100100100
sa100100100
sah100100100
scn100100100
sco100100100
sd100100100
sh200001000010000
si100100100
simple2000010001000
sk200001000010000
sl150001000010000
so100100100
sq500010001000
sr200001000010000
su100100100
sv200001000010000
sw100010001000
szl100100100
ta1500010001000
te100010001000
tg100100100
th200001000010000
tk100100100
tl1000010001000
tr200001000010000
tt100010001000
ug100100100
uk200001000010000
ur2000010001000
uz100010001000
vec100100100
vep100100100
vi200001000010000
vls100100100
vo100100100
wa100100100
war100100100
wuu100100100
xmf100100100
yi100100100
yo100100100
zea100100100
zh200001000010000
zh-classical100100100
zh-min-nan100100100
zh-yue200001000010000

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 and @rabeehk for adding this dataset.