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---
license: mit
task_categories:
- text-classification
language:
- en
size_categories:
- 100K<n<1M
source_datasets:
- original
configs:
- config_name: default
data_files:
- split: train
path: train.zip
- split: validation
path: val.zip
- split: test
path: test.zip
pretty_name: MSC
---
## Dataset Description
The text data (title and abstract) of 164,230 arXiv preprints which are associated with at least one [MSC (mathematical subject classification)](https://en.wikipedia.org/wiki/Mathematics_Subject_Classification) code. Predicting 3-character MSC codes based on the cleaned text (processed title+abstarct) amounts to a multi-label classification task.
## Dataset Structure
- The column `cleaned_text` should be used as the input of the text classification task. This is obtained from processing the text data (titles and abstracts) of math-related preprints.
- The last 531 columns are one-hot encoded MSC classes, and should be used as target variables of the multi-label classification task.
- Other columns are auxiliary:
- `url`) the URL of the preprint (the latest version as of December 2023),
- `title`) the original title,
- `abstract`) the original abstract,
- `primary_category`) the primary [arXiv category](https://arxiv.org/category_taxonomy) (for this data, almost always a category of the math archive, or the mathematical physics archive).
- **Subtask**) Predicting `primary_category` based on `cleaned_text`, a multi-class text classification task with ~30 distinct labels.
## Data Splits
Stratified sampling was used for splitting the data so that the proportions of a target variable among the splits are not very different.
|Dataset |Description |Number of instances |
|---------|------------------|----------------------|
|main.zip |the whole data |164,230 |
|train.zip|the training set |104,675 |
|val.zip |the validation set|18,540 |
|test.zip |the test set |41,015 |
## Data Collection and Cleaning
The details are outlined in this [notebook](https://github.com/FilomKhash/Math-Preprint-Classifier/blob/main/Scarping%20and%20Cleaning%20the%20Data.ipynb).
As for the raw data, with the help of the [arxiv package](https://pypi.org/project/arxiv/), we scraped preprints listed, or cross-listed, under the math archive. This raw data was then processed:
- dropping preprints with an abnormally high number of versions,
- keeping only the last arXiv version,
- dropping preprints whose metadata does not include any MSC class,
- dropping entries with pre-2010 mathematics subject classification convention,
- concatenating abstract and title strings and carrying out the following steps to obtain the `cleaned_text` column:
- removing the LaTeX math environment and URL citations,
- make the text lower case, normalizing accents and removing special characters,
- removing English and some corpus-specific stop words,
- stemming.
## Citation
<https://github.com/FilomKhash/Math-Preprint-Classifier> |