Datasets:

unimelb-nlp
/

wikiann

	---
	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.