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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.09122 |
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| _version_ | 1866929403034861568 |
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| author | Mayhew, Stephen Blevins, Terra Liu, Shuheng Šuppa, Marek Gonen, Hila Imperial, Joseph Marvin Karlsson, Börje F. Lin, Peiqin Ljubešić, Nikola Miranda, LJ Plank, Barbara Riabi, Arij Pinter, Yuval |
| author_facet | Mayhew, Stephen Blevins, Terra Liu, Shuheng Šuppa, Marek Gonen, Hila Imperial, Joseph Marvin Karlsson, Börje F. Lin, Peiqin Ljubešić, Nikola Miranda, LJ Plank, Barbara Riabi, Arij Pinter, Yuval |
| contents | We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We release the data, code, and fitted models to the public. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09122 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark Mayhew, Stephen Blevins, Terra Liu, Shuheng Šuppa, Marek Gonen, Hila Imperial, Joseph Marvin Karlsson, Börje F. Lin, Peiqin Ljubešić, Nikola Miranda, LJ Plank, Barbara Riabi, Arij Pinter, Yuval Computation and Language We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We release the data, code, and fitted models to the public. |
| title | Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.09122 |