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+ ---
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+ annotations_creators:
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+ - machine-generated
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+ language_creators:
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+ - machine-generated
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+ languages:
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+ - de
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+ - en
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+ - es
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+ - fr
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+ - it
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+ - nl
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+ - pl
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+ - pt
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+ - ru
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+ licenses:
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+ - cc-by-nc-sa-4.0
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+ pretty_name: wikineural-dataset
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - structure-prediction
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+ task_ids:
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+ - named-entity-recognition
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+ ---
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+
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+ ## Model Description
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+
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+ - **Summary:** mBERT model fine-tuned on the recently-introduced WikiNEuRal dataset for Multilingual NER.
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+ - **Official Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural)
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+ - **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/)
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+
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+ ## Licensing Information
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+
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+ Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
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+
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+ ## Citation Information
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+
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+ ```bibtex
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+ @inproceedings{tedeschi-etal-2021-wikineural-combined,
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+ title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
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+ author = "Tedeschi, Simone and
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+ Maiorca, Valentino and
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+ Campolungo, Niccol{\`o} and
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+ Cecconi, Francesco and
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+ Navigli, Roberto",
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+ booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
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+ month = nov,
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+ year = "2021",
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+ address = "Punta Cana, Dominican Republic",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2021.findings-emnlp.215",
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+ pages = "2521--2533",
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+ 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.",
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+ }
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+ ```
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+