|
--- |
|
annotations_creators: |
|
- machine-generated |
|
language_creators: |
|
- machine-generated |
|
languages: |
|
- de |
|
- en |
|
- es |
|
- fr |
|
- it |
|
- nl |
|
- pl |
|
- pt |
|
- ru |
|
licenses: |
|
- cc-by-nc-sa-4.0 |
|
pretty_name: wikineural-dataset |
|
source_datasets: |
|
- original |
|
task_categories: |
|
- structure-prediction |
|
task_ids: |
|
- named-entity-recognition |
|
|
|
--- |
|
|
|
## Model Description |
|
|
|
- **Summary:** mBERT model fine-tuned for 3 epochs on the recently-introduced WikiNEuRal dataset for Multilingual NER. The system supports the 9 languages covered by WikiNEuRal, and it was trained on all 9 languages jointly. |
|
- **Official Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural) |
|
- **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/) |
|
|
|
## Licensing Information |
|
|
|
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 and models belongs to the original copyright holders. |
|
|
|
## Citation Information |
|
|
|
```bibtex |
|
@inproceedings{tedeschi-etal-2021-wikineural-combined, |
|
title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}", |
|
author = "Tedeschi, Simone and |
|
Maiorca, Valentino and |
|
Campolungo, Niccol{\`o} and |
|
Cecconi, Francesco and |
|
Navigli, Roberto", |
|
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", |
|
month = nov, |
|
year = "2021", |
|
address = "Punta Cana, Dominican Republic", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://aclanthology.org/2021.findings-emnlp.215", |
|
pages = "2521--2533", |
|
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.", |
|
} |
|
``` |
|
|
|
|
