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Datasets:
tner
/
multinerd

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
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
pretty_name: MultiNERD

Dataset Card for "tner/multinerd"

Dataset Description

Dataset Summary

MultiNERD NER benchmark 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, SUPER, PHY

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-SUPER": 31,
"I-SUPER": 32,
"B-PHY": 33,
"I-PHY": 34
}

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-navigli-2022-multinerd,
    title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)",
    author = "Tedeschi, Simone  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-naacl.60",
    doi = "10.18653/v1/2022.findings-naacl.60",
    pages = "801--812",
    abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.",
}