Datasets:
metadata
annotations_creators:
- no-annotation
language:
- zh
- en
license:
- apache-2.0
pretty_name: CSL
size_categories:
- 100K<n<1M
source_datasets:
- extended|csl
tags: []
task_categories:
- text-retrieval
task_ids:
- document-retrieval
Dataset Card for CSL
Dataset Description
CSL is the Chinese Scientific Literature Dataset.
- Paper: https://aclanthology.org/2022.coling-1.344
- Repository: https://github.com/ydli-ai/CSL
Dataset Summary
The dataset contains titles, abstracts, keywords of papers written in Chinese from several academic fields.
Languages
- Chinese
- English (translation)
Dataset Structure
Data Instances
Split | Documents |
---|---|
csl |
396k |
en_translation |
396k |
Data Fields
doc_id
: unique identifier for this documenttitle
: title of the paperabstract
: abstract of the paperkeywords
: keywords associated with the papercategory
: the broad category of the papercategory_eng
: English translaction of the broad category (e.g., Engineering)discipline
: academic discipline of the paperdiscipline_eng
: English translation of the academic discipline (e.g., Agricultural Engineering)
The en_translation
contains documents translated from Google Translation service.
All text are in English, so the fields category_eng
and discipline_eng
are omitted.
Dataset Usage
Using 🤗 Datasets:
from datasets import load_dataset
dataset = load_dataset('neuclir/csl')['csl']
License & Citation
This dataset is based off the Chinese Scientific Literature Dataset under Apache 2.0.
The primay change is the addition of doc_id
s, English translactions of the category and discipline descriptions by a native speaker,
and basic de-duplication. Code that performed this modification is avalable in this repository.
If you use this data, please cite:
@inproceedings{li-etal-2022-csl,
title = "{CSL}: A Large-scale {C}hinese Scientific Literature Dataset",
author = "Li, Yudong and
Zhang, Yuqing and
Zhao, Zhe and
Shen, Linlin and
Liu, Weijie and
Mao, Weiquan and
Zhang, Hui",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.344",
pages = "3917--3923",
}