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 looks as follows.

{
    'tokens': ['I', 'hate', 'the', 'words', 'chunder', ',', 'vomit', 'and', 'puke', '.', 'BUUH', '.'],
    'tags': [6, 6, 6, 6, 6, 6, 6, 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

name	train	validation	test
btc	6338	1001	2000

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.",
}