Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Sub-tasks:
named-entity-recognition
Size:
1M - 10M
Tags:
structure-prediction
License:
annotations_creators: | |
- machine-generated | |
language_creators: | |
- machine-generated | |
language: | |
- de | |
- en | |
- es | |
- fr | |
- it | |
- nl | |
- pl | |
- pt | |
- ru | |
- zh | |
license: | |
- cc-by-nc-sa-4.0 | |
multilinguality: | |
- multilingual | |
source_datasets: | |
- original | |
task_categories: | |
- token-classification | |
task_ids: | |
- named-entity-recognition | |
pretty_name: multinerd-dataset | |
tags: | |
- structure-prediction | |
dataset_info: | |
- config_name: de | |
features: | |
- name: tokens | |
sequence: string | |
- name: ner_tags | |
splits: | |
- name: train | |
- name: validation | |
- name: test | |
- config_name: en | |
features: | |
- name: tokens | |
sequence: string | |
- name: ner_tags | |
splits: | |
- name: train | |
- name: validation | |
- name: test | |
- config_name: es | |
features: | |
- name: tokens | |
sequence: string | |
- name: ner_tags | |
splits: | |
- name: train | |
- name: validation | |
- name: test | |
- config_name: fr | |
features: | |
- name: tokens | |
sequence: string | |
- name: ner_tags | |
splits: | |
- name: train | |
- name: validation | |
- name: test | |
- config_name: it | |
features: | |
- name: tokens | |
sequence: string | |
- name: ner_tags | |
splits: | |
- name: train | |
- name: validation | |
- name: test | |
## Table of Contents | |
- [Description](#description) | |
- [Dataset Structure](#dataset-structure) | |
- [Additional Information](#additional-information) | |
## Dataset Card for MultiNERD dataset | |
## Dataset Description | |
- **Summary:** Training data for fine-grained NER in 10 languages. | |
- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd) | |
- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/) | |
- **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com) | |
## Description | |
- **Summary:** In a nutshell, MultiNERD is the first **language-agnostic** methodology for automatically creating **multilingual, multi-genre and fine-grained annotations** for **Named Entity Recognition** and **Entity Disambiguation**. Specifically, it can be seen an extension of the combination of two prior works from our research group that are [WikiNEuRal](https://www.github.com/Babelscape/wikineural), from which we took inspiration for the state-of-the-art silver-data creation methodology, and [NER4EL](https://www.github.com/Babelscape/NER4EL), from which we took the fine-grained classes and inspiration for the entity linking part. The produced dataset covers: **10 languages** (Chinese, Dutch, English, French, German, Italian, Polish, Portuguese, Russian and Spanish), **15 NER categories** (Person (PER), Location (LOC), Organization (ORG}), Animal (ANIM), Biological entity (BIO), Celestial Body (CEL), Disease (DIS), Event (EVE), Food (FOOD), Instrument (INST), Media (MEDIA), Plant (PLANT), Mythological entity (MYTH), Time (TIME) and Vehicle (VEHI)), and **2 textual genres** ([Wikipedia](https://www.wikipedia.org/) and [WikiNews](https://www.wikinews.org/)); | |
- **Repository:** [https://github.com/Babelscape/multinerd](https://github.com/Babelscape/multinerd) | |
- **Paper:** [https://aclanthology.org/multinerd](https://aclanthology.org/2022.findings-naacl.60/) | |
- **Point of Contact:** [tedeschi@babelscape.com](tedeschi@babelscape.com) | |
## Dataset Structure | |
The data fields are the same among all splits. | |
- `tokens`: a `list` of `string` features. | |
- `ner_tags`: a `list` of classification labels (`int`). | |
- `lang`: a `string` feature. Full list of language: Chinese (zh), Dutch (nl), English (en), French (fr), German (de), Italian (it), Polish (pl), Portugues (pt), Russian (ru), Spanish (es). | |
- The full tagset with indices is reported below: | |
```python | |
{ | |
"O": 0, | |
"B-PER": 1, | |
"I-PER": 2, | |
"B-ORG": 3, | |
"I-ORG": 4, | |
"B-LOC": 5, | |
"I-LOC": 6, | |
"B-ANIM": 7, | |
"I-ANIM": 8, | |
"B-BIO": 9, | |
"I-BIO": 10, | |
"B-CEL": 11, | |
"I-CEL": 12, | |
"B-DIS": 13, | |
"I-DIS": 14, | |
"B-EVE": 15, | |
"I-EVE": 16, | |
"B-FOOD": 17, | |
"I-FOOD": 18, | |
"B-INST": 19, | |
"I-INST": 20, | |
"B-MEDIA": 21, | |
"I-MEDIA": 22, | |
"B-MYTH": 23, | |
"I-MYTH": 24, | |
"B-PLANT": 25, | |
"I-PLANT": 26, | |
"B-TIME": 27, | |
"I-TIME": 28, | |
"B-VEHI": 29, | |
"I-VEHI": 30, | |
} | |
``` | |
## Additional Information | |
- **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 belongs to the original copyright holders. | |
- **Citation Information**: Please consider citing our work if you use data and/or code from this repository. | |
```bibtex | |
@inproceedings{tedeschi-navigli-2022-multinerd, | |
title = "{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)", | |
author = "Tedeschi, Simone and | |
Navigli, Roberto", | |
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022", | |
month = jul, | |
year = "2022", | |
address = "Seattle, United States", | |
publisher = "Association for Computational Linguistics", | |
url = "https://aclanthology.org/2022.findings-naacl.60", | |
doi = "10.18653/v1/2022.findings-naacl.60", | |
pages = "801--812", | |
abstract = "Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems.In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres.We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems.We release our dataset at https://github.com/Babelscape/multinerd.", | |
} | |
``` | |
- **Contributions**: Thanks to [@sted97](https://github.com/sted97) for adding this dataset. | |