Dataset Card for "tner/wikineural"

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.",
}