OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914205993533440 |
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| author | Palen-Michel, Chester Pickering, Maxwell Kruse, Maya Sälevä, Jonne Lignos, Constantine |
| author_facet | Palen-Michel, Chester Pickering, Maxwell Kruse, Maya Sälevä, Jonne Lignos, Constantine |
| contents | We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets. OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies. We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER. We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER. We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task. OpenNER is released at https://github.com/bltlab/open-ner. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09587 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages Palen-Michel, Chester Pickering, Maxwell Kruse, Maya Sälevä, Jonne Lignos, Constantine Computation and Language We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets. OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies. We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER. We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER. We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task. OpenNER is released at https://github.com/bltlab/open-ner. |
| title | OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50+ Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.09587 |