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

Repository: T-NER
Paper: https://aclanthology.org/2021.findings-emnlp.215/
Dataset: WikiNeural
Domain: Wikipedia
Number of Entity: 16

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 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.

{
"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

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.",
}