metadata

language:
  - ace
  - bg
  - da
  - fur
  - ilo
  - lij
  - mzn
  - qu
  - su
  - vi
  - af
  - bh
  - de
  - fy
  - io
  - lmo
  - nap
  - rm
  - sv
  - vls
  - als
  - bn
  - diq
  - ga
  - is
  - ln
  - nds
  - ro
  - sw
  - vo
  - am
  - bo
  - dv
  - gan
  - it
  - lt
  - ne
  - ru
  - szl
  - wa
  - an
  - br
  - el
  - gd
  - ja
  - lv
  - nl
  - rw
  - ta
  - war
  - ang
  - bs
  - eml
  - gl
  - jbo
  - nn
  - sa
  - te
  - wuu
  - ar
  - ca
  - en
  - gn
  - jv
  - mg
  - 'no'
  - sah
  - tg
  - xmf
  - arc
  - eo
  - gu
  - ka
  - mhr
  - nov
  - scn
  - th
  - yi
  - arz
  - cdo
  - es
  - hak
  - kk
  - mi
  - oc
  - sco
  - tk
  - yo
  - as
  - ce
  - et
  - he
  - km
  - min
  - or
  - sd
  - tl
  - zea
  - ast
  - ceb
  - eu
  - hi
  - kn
  - mk
  - os
  - sh
  - tr
  - ay
  - ckb
  - ext
  - hr
  - ko
  - ml
  - pa
  - si
  - tt
  - az
  - co
  - fa
  - hsb
  - ksh
  - mn
  - pdc
  - ug
  - ba
  - crh
  - fi
  - hu
  - ku
  - mr
  - pl
  - sk
  - uk
  - zh
  - bar
  - cs
  - hy
  - ky
  - ms
  - pms
  - sl
  - ur
  - csb
  - fo
  - ia
  - la
  - mt
  - pnb
  - so
  - uz
  - cv
  - fr
  - id
  - lb
  - mwl
  - ps
  - sq
  - vec
  - be
  - cy
  - frr
  - ig
  - li
  - my
  - pt
  - sr
multilinguality:
  - multilingual
size_categories:
  - 10K<100k
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: WikiAnn

Dataset Card for "tner/wikiann"

Dataset Description

Repository: T-NER
Paper: https://aclanthology.org/P17-1178/
Dataset: WikiAnn
Domain: Wikipedia
Number of Entity: 3

Dataset Summary

WikiAnn NER dataset formatted in a part of TNER project.

Entity Types: LOC, ORG, PER

Dataset Structure

Data Instances

An example of train of ja looks as follows.

{
  'tokens': ['#', '#', 'ユ', 'リ', 'ウ', 'ス', '・', 'ベ', 'ー', 'リ', 'ッ', 'ク', '#', '1', '9','9','9'],
  'tags': [6, 6, 2, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]
}

Label ID

The label2id dictionary can be found at here.

{
    "B-LOC": 0,
    "B-ORG": 1,
    "B-PER": 2,
    "I-LOC": 3,
    "I-ORG": 4,
    "I-PER": 5,
    "O": 6
}

Data Splits

language	train	validation	test
ace	100	100	100
bg	20000	10000	10000
da	20000	10000	10000
fur	100	100	100
ilo	100	100	100
lij	100	100	100
mzn	100	100	100
qu	100	100	100
su	100	100	100
vi	20000	10000	10000
af	5000	1000	1000
bh	100	100	100
de	20000	10000	10000
fy	1000	1000	1000
io	100	100	100
lmo	100	100	100
nap	100	100	100
rm	100	100	100
sv	20000	10000	10000
vls	100	100	100
als	100	100	100
bn	10000	1000	1000
diq	100	100	100
ga	1000	1000	1000
is	1000	1000	1000
ln	100	100	100
nds	100	100	100
ro	20000	10000	10000
sw	1000	1000	1000
vo	100	100	100
am	100	100	100
bo	100	100	100
dv	100	100	100
gan	100	100	100
it	20000	10000	10000
lt	10000	10000	10000
ne	100	100	100
ru	20000	10000	10000
szl	100	100	100
wa	100	100	100
an	1000	1000	1000
br	1000	1000	1000
el	20000	10000	10000
gd	100	100	100
ja	20000	10000	10000
lv	10000	10000	10000
nl	20000	10000	10000
rw	100	100	100
ta	15000	1000	1000
war	100	100	100
ang	100	100	100
bs	15000	1000	1000
eml	100	100	100
gl	15000	10000	10000
jbo	100	100	100
map-bms	100	100	100
nn	20000	1000	1000
sa	100	100	100
te	1000	1000	1000
wuu	100	100	100
ar	20000	10000	10000
ca	20000	10000	10000
en	20000	10000	10000
gn	100	100	100
jv	100	100	100
mg	100	100	100
no	20000	10000	10000
sah	100	100	100
tg	100	100	100
xmf	100	100	100
arc	100	100	100
cbk-zam	100	100	100
eo	15000	10000	10000
gu	100	100	100
ka	10000	10000	10000
mhr	100	100	100
nov	100	100	100
scn	100	100	100
th	20000	10000	10000
yi	100	100	100
arz	100	100	100
cdo	100	100	100
es	20000	10000	10000
hak	100	100	100
kk	1000	1000	1000
mi	100	100	100
oc	100	100	100
sco	100	100	100
tk	100	100	100
yo	100	100	100
as	100	100	100
ce	100	100	100
et	15000	10000	10000
he	20000	10000	10000
km	100	100	100
min	100	100	100
or	100	100	100
sd	100	100	100
tl	10000	1000	1000
zea	100	100	100
ast	1000	1000	1000
ceb	100	100	100
eu	10000	10000	10000
hi	5000	1000	1000
kn	100	100	100
mk	10000	1000	1000
os	100	100	100
sh	20000	10000	10000
tr	20000	10000	10000
zh-classical	100	100	100
ay	100	100	100
ckb	1000	1000	1000
ext	100	100	100
hr	20000	10000	10000
ko	20000	10000	10000
ml	10000	1000	1000
pa	100	100	100
si	100	100	100
tt	1000	1000	1000
zh-min-nan	100	100	100
az	10000	1000	1000
co	100	100	100
fa	20000	10000	10000
hsb	100	100	100
ksh	100	100	100
mn	100	100	100
pdc	100	100	100
simple	20000	1000	1000
ug	100	100	100
zh-yue	20000	10000	10000
ba	100	100	100
crh	100	100	100
fi	20000	10000	10000
hu	20000	10000	10000
ku	100	100	100
mr	5000	1000	1000
pl	20000	10000	10000
sk	20000	10000	10000
uk	20000	10000	10000
zh	20000	10000	10000
bar	100	100	100
cs	20000	10000	10000
fiu-vro	100	100	100
hy	15000	1000	1000
ky	100	100	100
ms	20000	1000	1000
pms	100	100	100
sl	15000	10000	10000
ur	20000	1000	1000
bat-smg	100	100	100
csb	100	100	100
fo	100	100	100
ia	100	100	100
la	5000	1000	1000
mt	100	100	100
pnb	100	100	100
so	100	100	100
uz	1000	1000	1000
be-x-old	5000	1000	1000
cv	100	100	100
fr	20000	10000	10000
id	20000	10000	10000
lb	5000	1000	1000
mwl	100	100	100
ps	100	100	100
sq	5000	1000	1000
vec	100	100	100
be	15000	1000	1000
cy	10000	1000	1000
frr	100	100	100
ig	100	100	100
li	100	100	100
my	100	100	100
pt	20000	10000	10000
sr	20000	10000	10000
vep	100	100	100

Citation Information

@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://aclanthology.org/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.",
}