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Datasets:
tner
/
wikineural

Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
German
English
Spanish
Size:
1M - 10M
Libraries:
Datasets
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metadata
language:
  - de
  - en
  - es
  - fr
  - it
  - nl
  - pl
  - pt
  - ru
multilinguality:
  - multilingual
size_categories:
  - 10K<100k
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: WikiNeural

Dataset Card for "tner/wikineural"

Dataset Description

Dataset Summary

WikiAnn NER dataset formatted in a part of TNER project.

  • Entity Types: PER, LOC, ORG, ANIM, BIO, CEL, DIS, EVE, FOOD, INST, MEDIA, PLANT, MYTH, TIME, VEHI, MISC

Dataset Structure

Data Instances

An example of train of de looks as follows.

{
    'tokens': [ "Dieses", "wiederum", "basierte", "auf", "dem", "gleichnamigen", "Roman", "von", "Noël", "Calef", "." ],
    'tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0 ]
}

Label ID

The label2id dictionary can be found at here.

{
"O": 0,
"B-PER": 1,
"I-PER": 2,
"B-LOC": 3,
"I-LOC": 4,
"B-ORG": 5,
"I-ORG": 6,
"B-ANIM": 7,
"I-ANIM": 8,
"B-BIO": 9,
"I-BIO": 10,
"B-CEL": 11,
"I-CEL": 12,
"B-DIS": 13,
"I-DIS": 14,
"B-EVE": 15,
"I-EVE": 16,
"B-FOOD": 17,
"I-FOOD": 18,
"B-INST": 19,
"I-INST": 20,
"B-MEDIA": 21,
"I-MEDIA": 22,
"B-PLANT": 23,
"I-PLANT": 24,
"B-MYTH": 25,
"I-MYTH": 26,
"B-TIME": 27,
"I-TIME": 28,
"B-VEHI": 29,
"I-VEHI": 30,
"B-MISC": 31,
"I-MISC": 32
}

Data Splits

language train validation test
de 98640 12330 12372
en 92720 11590 11597
es 76320 9540 9618
fr 100800 12600 12678
it 88400 11050 11069
nl 83680 10460 10547
pl 108160 13520 13585
pt 80560 10070 10160
ru 92320 11540 11580

Citation Information 

@inproceedings{tedeschi-etal-2021-wikineural-combined,
    title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
    author = "Tedeschi, Simone  and
      Maiorca, Valentino  and
      Campolungo, Niccol{\`o}  and
      Cecconi, Francesco  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-emnlp.215",
    doi = "10.18653/v1/2021.findings-emnlp.215",
    pages = "2521--2533",
    abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
}