Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
File size: 12,687 Bytes
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---
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](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/P17-1178/](https://aclanthology.org/P17-1178/)
- **Dataset:** WikiAnn
- **Domain:** Wikipedia
- **Number of Entity:** 3
### Dataset Summary
WikiAnn NER dataset formatted in a part of [TNER](https://github.com/asahi417/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](https://huggingface.co/datasets/tner/wikiann/raw/main/dataset/label.json).
```python
{
"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.",
}
``` |