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Datasets:
FilomKhash
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Mathematical_Subject_Class_Prediction

Tasks:
Text Classification
Languages:
English
Size:
100K<n<1M
License:
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Mathematical_Subject_Class_Prediction / README.md
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metadata
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) 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 (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. As for the raw data, with the help of the arxiv package, 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