--- 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/btc/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.", } ```