DFKI-SLT/few-nerd

Dataset Card for "Few-NERD"

Table of Contents

• —Dataset Description
• —Dataset Summary
• —Supported Tasks and Leaderboards
• —Languages
• —Dataset Structure
• —Data Instances
• —Data Fields
• —Data Splits
• —Dataset Creation
• —Curation Rationale
• —Source Data
• —Annotations
• —Personal and Sensitive Information
• —Considerations for Using the Data
• —Social Impact of Dataset
• —Discussion of Biases
• —Other Known Limitations
• —Additional Information
• —Dataset Curators
• —Licensing Information
• —Citation Information
• —Contributions

Dataset Description

• —Homepage: https://ningding97.github.io/fewnerd/
• —Repository: https://github.com/thunlp/Few-NERD
• —Paper: https://aclanthology.org/2021.acl-long.248/
• —Point of Contact: See https://ningding97.github.io/fewnerd/

Dataset Summary

This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/.

Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).

NER tags use the IO tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.

For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.

Supported Tasks and Leaderboards

• —Tasks: Named Entity Recognition, Few-shot NER
• —Leaderboards:
• —https://ningding97.github.io/fewnerd/
• —named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup
• —other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra
• —other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter

Languages

English

Dataset Structure

Data Instances

• —Size of downloaded dataset files:
• —super: 14.6 MB
• —intra: 11.4 MB
• —inter: 11.5 MB

• —Size of the generated dataset:
• —super: 116.9 MB
• —intra: 106.2 MB
• —inter: 106.2 MB

• —Total amount of disk used: 366.8 MB

An example of 'train' looks as follows.

{
    'id': '1', 
    'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'], 
    'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0], 
    'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}

Data Fields

The data fields are the same among all splits.

• —id: a string feature.
• —tokens: a list of string features.
• —ner_tags: a list of classification labels, with possible values including O (0), art (1), building (2), event (3), location (4), organization (5), other(6), person (7), product (8)
• —fine_ner_tags: a list of fine-grained classification labels, with possible values including O (0), art-broadcastprogram (1), art-film (2), ...

Data Splits

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

CC BY-SA 4.0 license

Citation Information

@inproceedings{ding-etal-2021-nerd,
    title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
    author = "Ding, Ning  and
      Xu, Guangwei  and
      Chen, Yulin  and
      Wang, Xiaobin  and
      Han, Xu  and
      Xie, Pengjun  and
      Zheng, Haitao  and
      Liu, Zhiyuan",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.248",
    doi = "10.18653/v1/2021.acl-long.248",
    pages = "3198--3213",
}

Contributions