metadata

annotations_creators:
  - machine-generated
language:
  - en
language_creators:
  - found
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
pretty_name: unarXive IMRaD classification
size_categories:
  - 100K<n<1M
tags:
  - arXiv.org
  - arXiv
  - IMRaD
  - publication
  - paper
  - preprint
  - section
  - physics
  - mathematics
  - computer science
  - cs
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
source_datasets:
  - extended|10.5281/zenodo.7752615
dataset_info:
  features:
    - name: _id
      dtype: string
    - name: text
      dtype: string
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 451908280
      num_examples: 520053
    - name: test
      num_bytes: 4650429
      num_examples: 5000
    - name: validation
      num_bytes: 4315597
      num_examples: 5001
  download_size: 482376743
  dataset_size: 460874306

Dataset Card for unarXive IMRaD classification

Dataset Description

Homepage: https://github.com/IllDepence/unarXive
Paper: unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network

Dataset Summary

The unarXive IMRaD classification dataset contains 530k paragraphs from computer science papers and the IMRaD section they originate from. The paragraphs are derived from unarXive.

The dataset can be used as follows.

from datasets import load_dataset

imrad_data = load_dataset('saier/unarXive_imrad_clf')
imrad_data = imrad_data.class_encode_column('label')  # assign target label column
imrad_data = imrad_data.remove_columns('_id')         # remove sample ID column

Dataset Structure

Data Instances

Each data instance contains the paragraph’s text as well as one of the labels ('i', 'm', 'r', 'd', 'w' — for Introduction, Methods, Results, Discussion and Related Work). An example is shown below.

{'_id': '789f68e7-a1cc-4072-b07d-ecffc3e7ca38',
 'label': 'm',
 'text': 'To link the mentions encoded by BERT to the KGE entities, we define '
         'an entity linking loss as cross-entropy between self-supervised '
         'entity labels and similarities obtained from the linker in KGE '
         'space:\n'
         '\\(\\mathcal {L}_{EL}=\\sum -\\log \\dfrac{\\exp (h_m^{proj}\\cdot '
         '\\textbf {e})}{\\sum _{\\textbf {e}_j\\in \\mathcal {E}} \\exp '
         '(h_m^{proj}\\cdot \\textbf {e}_j)}\\) \n'}

Data Splits

The data is split into training, development, and testing data as follows.

Training: 520,053 instances
Development: 5000 instances
Testing: 5001 instances

Dataset Creation

Source Data

The paragraph texts are extracted from the data set unarXive.

Who are the source language producers?

The paragraphs were written by the authors of the arXiv papers. In file license_info.jsonl author and text licensing information can be found for all samples, An example is shown below.


{'authors': 'Yusuke Sekikawa, Teppei Suzuki',
 'license': 'http://creativecommons.org/licenses/by/4.0/',
 'paper_arxiv_id': '2011.09852',
 'sample_ids': ['cc375518-347c-43d0-bfb2-f88564d66df8',
                '18dc073e-a48e-488e-b34c-e5fc3cb8a4ca',
                '0c2e89b3-d863-4bc2-9e11-8f6c48d867cb',
                'd85e46cf-b11d-49b6-801b-089aa2dd037d',
                '92915cea-17ab-4a98-aad2-417f6cdd53d2',
                'e88cb422-47b7-4f69-9b0b-fbddf8140d98',
                '4f5094a4-0e6e-46ae-a34d-e15ce0b9803c',
                '59003494-096f-4a7c-ad65-342b74eed561',
                '6a99b3f5-217e-4d3d-a770-693483ef8670']}

Annotations

Class labels were automatically determined (see implementation).

Considerations for Using the Data

Discussion and Biases

Because only paragraphs unambiguously assignable to one of the IMRaD classeswere used, a certain selection bias is to be expected in the data.

Other Known Limitations

Depending on authors’ writing styles as well LaTeX processing quirks, paragraphs can vary in length a significantly.

Additional Information

Licensing information

The dataset is released under the Creative Commons Attribution-ShareAlike 4.0.

Citation Information

@inproceedings{Saier2023unarXive,
  author        = {Saier, Tarek and Krause, Johan and F\"{a}rber, Michael},
  title         = {{unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network}},
  booktitle     = {Proceedings of the 23rd ACM/IEEE Joint Conference on Digital Libraries},
  year          = {2023},
  series        = {JCDL '23}
}