README.md

---
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](https://github.com/IllDepence/unarXive)
* **Paper:** [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://arxiv.org/abs/2303.14957)

### 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](https://github.com/IllDepence/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](https://github.com/IllDepence/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](https://github.com/IllDepence/unarXive/blob/master/src/utility_scripts/ml_tasks_prep_data.py)).

## 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}
}
```