metadata

annotations_creators:
  - crowdsourced
  - found
language:
  - zh
language_creators:
  - found
  - crowdsourced
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
paperswithcode_id: hansel
pretty_name: Hansel
size_categories:
  - 1M<n<10M
  - 1K<n<10K
source_datasets:
  - original
tags: []
task_categories:
  - text-retrieval
task_ids:
  - entity-linking-retrieval
dataset_info:
  - config_name: wiki
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
      - name: start
        dtype: int64
      - name: end
        dtype: int64
      - name: mention
        dtype: string
      - name: gold_id
        dtype: string
    splits:
      - name: train
      - name: validation
  - config_name: hansel-few-shot
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
      - name: start
        dtype: int64
      - name: end
        dtype: int64
      - name: mention
        dtype: string
      - name: gold_id
        dtype: string
      - name: source
        dtype: string
      - name: domain
        dtype: string
    splits:
      - name: test
  - config_name: hansel-zero-shot
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
      - name: start
        dtype: int64
      - name: end
        dtype: int64
      - name: mention
        dtype: string
      - name: gold_id
        dtype: string
      - name: source
        dtype: string
      - name: domain
        dtype: string
    splits:
      - name: test

Dataset Card for "Hansel"

Table of Contents
Dataset Description
Dataset Structure
- Data Instances
- Data Splits
Citation

Dataset Description

Homepage: https://github.com/HITsz-TMG/Hansel
Paper: https://arxiv.org/abs/2207.13005

Hansel is a high-quality human-annotated Chinese entity linking (EL) dataset, focusing on tail entities and emerging entities:

The test set contains Few-shot (FS) and zero-shot (ZS) slices, has 10K examples and uses Wikidata as the corresponding knowledge base.
The training and validation sets are from Wikipedia hyperlinks, useful for large-scale pretraining of Chinese EL systems.

Please see our WSDM 2023 paper "Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark" to learn more about our dataset.

For models in the paper and our processed knowledge base, please see our Github repository.

Dataset Structure

Data Instances

{"id": "hansel-eval-zs-1463", 
 "text": "1905电影网讯 已经筹备了十余年的吉尔莫·德尔·托罗的《匹诺曹》，在上个月顺利被网飞公司买下，成为了流媒体巨头旗下的新片。近日，这部备受关注的影片确定了自己的档期：2021年。虽然具体时间未定，但影片却已经实实在在地向前迈出了一步。", 
 "start": 29, 
 "end": 32, 
 "mention": "匹诺曹", 
 "gold_id": "Q73895818", 
 "source": "https://www.1905.com/news/20181107/1325389.shtml", 
 "domain": "news"
}

Data Splits

	# Mentions	# Entities	Domain
Train	9,879,813	541,058	Wikipedia
Validation	9,674	6,320	Wikipedia
Hansel-FS	5,260	2,720	News, Social Media
Hansel-ZS	4,715	4,046	News, Social Media, E-books, etc.

Citation

If you find our dataset useful, please cite us.

@inproceedings{xu2022hansel,
  author = {Xu, Zhenran and Shan, Zifei and Li, Yuxin and Hu, Baotian and Qin, Bing},
  title = {Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark},
  year = {2023},
  publisher = {Association for Computing Machinery},
  url = {https://doi.org/10.1145/3539597.3570418},
  booktitle = {Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining},
  pages = {832–840}
}