Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Size:
< 1K
License:
File size: 3,217 Bytes
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---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: string
- name: url
dtype: string
splits:
- name: test_de
num_bytes: 164433
num_examples: 200
- name: test_fr
num_bytes: 186036
num_examples: 200
- name: test_it
num_bytes: 197513
num_examples: 200
- name: test_rm
num_bytes: 206644
num_examples: 200
download_size: 220352
dataset_size: 754626
license: cc-by-4.0
task_categories:
- token-classification
task_ids:
- named-entity-recognition
language:
- de
- fr
- it
- rm
multilinguality:
- multilingual
pretty_name: SwissNER
size_categories:
- n<1K
---
# SwissNER
A multilingual test set for named entity recognition (NER) on Swiss news articles.
## Description
SwissNER is a dataset for named entity recognition based on manually annotated news articles in Swiss Standard German, French, Italian, and Romansh Grischun.
We have manually annotated a selection of articles that have been published in February 2023 in the categories "Switzerland" or "Regional" on the following online news portals:
- Swiss Standard German: [srf.ch](https://www.srf.ch/)
- French: [rts.ch](https://www.rts.ch/)
- Italian: [rsi.ch](https://www.rsi.ch/)
- Romansh Grischun: [rtr.ch](https://www.rtr.ch/)
For each article we extracted the first two paragraphs after the lead paragraph.
We followed the guidelines of the CoNLL-2002 and 2003 shared tasks and annotated the names of persons, organizations, locations and miscellaneous entities.
The annotation was performed by a single annotator.
When using this dataset, please consider citing our paper, ["SwissBERT: The Multilingual Language Model for Switzerland"](https://aclanthology.org/2023.swisstext-1.6/) (SwissText 2023).
## License
- Text paragraphs: © Swiss Broadcasting Corporation (SRG SSR)
- Annotations: Attribution 4.0 International (CC BY 4.0)
## Statistics
| | DE | FR | IT | RM | Total |
|----------------------|-----:|------:|------:|------:|------:|
| Number of paragraphs | 200 | 200 | 200 | 200 | 800 |
| Number of tokens | 9498 | 11434 | 12423 | 13356 | 46711 |
| Number of entities | 479 | 475 | 556 | 591 | 2101 |
| – `PER` | 104 | 92 | 93 | 118 | 407 |
| – `ORG` | 193 | 216 | 266 | 227 | 902 |
| – `LOC` | 182 | 167 | 197 | 246 | 792 |
| – `MISC` | 113 | 79 | 88 | 39 | 319 |
## Citation
```bibtex
@inproceedings{vamvas-etal-2023-swissbert,
title = "{S}wiss{BERT}: The Multilingual Language Model for {S}witzerland",
author = {Vamvas, Jannis and
Gra{\"e}n, Johannes and
Sennrich, Rico},
editor = {Ghorbel, Hatem and
Sokhn, Maria and
Cieliebak, Mark and
H{\"u}rlimann, Manuela and
de Salis, Emmanuel and
Guerne, Jonathan},
booktitle = "Proceedings of the 8th edition of the Swiss Text Analytics Conference",
month = jun,
year = "2023",
address = "Neuchatel, Switzerland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.swisstext-1.6",
pages = "54--69",
}
``` |