--- license: cc-by-sa-4.0 language: - ceb - da - de - en - hr - pt - ru - sk - sr - sv - tl - zh task_categories: - token-classification dataset_info: - config_name: ceb_gja features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 39540 num_examples: 188 download_size: 30395 dataset_size: 39540 - config_name: da_ddt features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 2304027 num_examples: 4383 - name: validation num_bytes: 293562 num_examples: 564 - name: test num_bytes: 285813 num_examples: 565 download_size: 2412623 dataset_size: 2883402 - config_name: de_pud features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 641819 num_examples: 1000 download_size: 501924 dataset_size: 641819 - config_name: en_ewt features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 6133506 num_examples: 12543 - name: validation num_bytes: 782835 num_examples: 2001 - name: test num_bytes: 785361 num_examples: 2077 download_size: 5962747 dataset_size: 7701702 - config_name: en_pud features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 600666 num_examples: 1000 download_size: 462120 dataset_size: 600666 - config_name: hr_set features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 4523323 num_examples: 6914 - name: validation num_bytes: 656738 num_examples: 960 - name: test num_bytes: 719703 num_examples: 1136 download_size: 4620262 dataset_size: 5899764 - config_name: pt_bosque features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 4839200 num_examples: 7018 - name: validation num_bytes: 802880 num_examples: 1172 - name: test num_bytes: 780768 num_examples: 1167 download_size: 4867264 dataset_size: 6422848 - config_name: pt_pud features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 661453 num_examples: 1000 download_size: 507495 dataset_size: 661453 - config_name: ru_pud features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 795294 num_examples: 1000 download_size: 669214 dataset_size: 795294 - config_name: sk_snk features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 2523121 num_examples: 8483 - name: validation num_bytes: 409448 num_examples: 1060 - name: test num_bytes: 411686 num_examples: 1061 download_size: 2597877 dataset_size: 3344255 - config_name: sr_set features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 2174631 num_examples: 3328 - name: validation num_bytes: 349276 num_examples: 536 - name: test num_bytes: 336065 num_examples: 520 download_size: 2248325 dataset_size: 2859972 - config_name: sv_pud features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 588564 num_examples: 1000 download_size: 464252 dataset_size: 588564 - config_name: sv_talbanken features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 2027488 num_examples: 4303 - name: validation num_bytes: 291774 num_examples: 504 - name: test num_bytes: 615209 num_examples: 1219 download_size: 2239432 dataset_size: 2934471 - config_name: tl_trg features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 23671 num_examples: 128 download_size: 18546 dataset_size: 23671 - config_name: tl_ugnayan features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 31732 num_examples: 94 download_size: 23941 dataset_size: 31732 - config_name: zh_gsd features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 2747999 num_examples: 3997 - name: validation num_bytes: 355515 num_examples: 500 - name: test num_bytes: 335893 num_examples: 500 download_size: 2614866 dataset_size: 3439407 - config_name: zh_gsdsimp features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: train num_bytes: 2747863 num_examples: 3997 - name: validation num_bytes: 352423 num_examples: 500 - name: test num_bytes: 335869 num_examples: 500 download_size: 2611290 dataset_size: 3436155 - config_name: zh_pud features: - name: idx dtype: string - name: text dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC - name: annotator sequence: string splits: - name: test num_bytes: 607418 num_examples: 1000 download_size: 460357 dataset_size: 607418 --- # Dataset Card for Universal NER ### Dataset Summary Universal NER (UNER) is an open, community-driven initiative aimed at creating gold-standard benchmarks for Named Entity Recognition (NER) across multiple languages. The primary objective of UNER is to offer high-quality, cross-lingually consistent annotations, thereby standardizing and advancing multilingual NER research. UNER v1 includes 19 datasets with named entity annotations, uniformly structured across 13 diverse languages. ### Supported Tasks and Leaderboards - `token-classification`: The dataset can be used to train token classification models of the NER variety. Some pre-trained models released as part of the UNER v1 release can be found at https://huggingface.co/universalner ### Languages The dataset contains data in the following languages: - Cebuano (`ceb`) - Danish (`da`) - German (`de`) - English (`en`) - Croatian (`hr`) - Portuguese (`pt`) - Russian (`ru`) - Slovak (`sk`) - Serbian (`sr`) - Swedish (`sv`) - Tagalog (`tl`) - Chinese (`zh`) ## Dataset Structure ### Data Instances An example from the `UNER_English-PUD` test set looks as follows ```json { "idx": "n01016-0002", "text": "Several analysts have suggested Huawei is best placed to benefit from Samsung's setback.", "tokens": [ "Several", "analysts", "have", "suggested", "Huawei", "is", "best", "placed", "to", "benefit", "from", "Samsung", "'s", "setback", "." ], "ner_tags": [ "O", "O", "O", "O", "B-ORG", "O", "O", "O", "O", "O", "O", "B-ORG", "O", "O", "O" ], "annotator": "blvns" } ``` ### Data Fields - `idx`: the ID uniquely identifying the sentence (instance), if available. - `text`: the full text of the sentence (instance) - `tokens`: the text of the sentence (instance) split into tokens. Note that this split is inhereted from Universal Dependencies - `ner_tags`: the NER tags associated with each one of the `tokens` - `annotator`: the annotator who provided the `ner_tags` for this particular instance ### Data Splits TBD ## Dataset Creation ### Curation Rationale TBD ### Source Data #### Initial Data Collection and Normalization We selected the Universal Dependency (UD) corpora as the default base texts for annotation due to their extensive language coverage, pre-existing data collection, cleaning, tokenization, and permissive licensing. This choice accelerates our process by providing a robust foundation. By adding another annotation layer to the already detailed UD annotations, we facilitate verification within our project and enable comprehensive multilingual research across the entire NLP pipeline. Given that UD annotations operate at the word level, we adopted the BIO annotation schema (specifically IOB2). In this schema, words forming the beginning (B) or inside (I) part of an entity (X ∈ {PER, LOC, ORG}) are annotated accordingly, while all other words receive an O tag. To maintain consistency, we preserve UD's original tokenization. Although UD serves as the default data source for UNER, the project is not restricted to UD corpora, particularly for languages not currently represented in UD. The primary requirement for inclusion in the UNER corpus is adherence to the UNER tagging guidelines. Additionally, we are open to converting existing NER efforts on UD treebanks to align with UNER. In this initial release, we have included four datasets transferred from other manual annotation efforts on UD sources (for DA, HR, ARABIZI, and SR). #### Who are the source language producers? This information can be found on per-dataset basis for each of the source Universal Dependencies datasets. ### Annotations #### Annotation process The data has been annotated by #### Who are the annotators? For the initial UNER annotation effort, we recruited volunteers from the multilingual NLP community via academic networks and social media. The annotators were coordinated through a Slack workspace, with all contributors working on a voluntary basis. We assume that annotators are either native speakers of the language they annotate or possess a high level of proficiency, although no formal language tests were conducted. The selection of the 13 dataset languages in the first UNER release was driven by the availability of annotators. As the project evolves, we anticipate the inclusion of additional languages and datasets as more annotators become available. ### Personal and Sensitive Information TBD ## Considerations for Using the Data ### Social Impact of Dataset TBD ### Discussion of Biases TBD ### Other Known Limitations TBD ## Additional Information ### Dataset Curators List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here. ### Licensing Information The UNER v1 is released under the terms of the [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/) license ### Citation Information If you use this dataset, please cite the corresponding [paper](https://aclanthology.org/2024.naacl-long.243): ``` @inproceedings{ mayhew2024universal, title={Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark}, author={Stephen Mayhew and Terra Blevins and Shuheng Liu and Marek Šuppa and Hila Gonen and Joseph Marvin Imperial and Börje F. Karlsson and Peiqin Lin and Nikola Ljubešić and LJ Miranda and Barbara Plank and Arij Riab and Yuval Pinter} booktitle={Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)}, year={2024}, url={https://aclanthology.org/2024.naacl-long.243/} } ```