{
 "id": "Babelscape/wikineural",
 "owner": "Babelscape",
 "name": "wikineural",
 "downloads": 2501,
 "likes": 38,
 "last_modified": "2022-11-13T07:52:46.000Z",
 "stated_licence": "cc-by-nc-sa-4.0",
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 "licence_name": null,
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 "gated": false,
 "tasks": [
  "token-classification"
 ],
 "citation": "@inproceedings{tedeschi-etal-2021-wikineural-combined,\n    title = \"{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}\",\n    author = \"Tedeschi, Simone  and\n      Maiorca, Valentino  and\n      Campolungo, Niccol{\\`o}  and\n      Cecconi, Francesco  and\n      Navigli, Roberto\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2021\",\n    month = nov,\n    year = \"2021\",\n    address = \"Punta Cana, Dominican Republic\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.findings-emnlp.215\",\n    pages = \"2521--2533\",\n    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.\",\n}",
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 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
 "revision": "74c9b9dca034bb1606a6769457983904bd976803",
 "stated_evidence": "https://huggingface.co/datasets/Babelscape/wikineural/blob/74c9b9dca034bb1606a6769457983904bd976803/README.md",
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}