README.md

metadata

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

Table of Contents

• Description
• Dataset Structure
• 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
• Paper: https://aclanthology.org/multinerd
• Point of Contact: 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, from which we took inspiration for the state-of-the-art silver-data creation methodology, and 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 and WikiNews);
• Repository: https://github.com/Babelscape/multinerd
• Paper: https://aclanthology.org/multinerd
• Point of Contact: 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:

{
    "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). 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.

@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 for adding this dataset.