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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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source_datasets: |
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- original |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: train.zip |
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- split: validation |
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path: val.zip |
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- split: test |
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path: test.zip |
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pretty_name: MSC |
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--- |
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## Dataset Description |
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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. |
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## Dataset Structure |
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- 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. |
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- The last 531 columns are one-hot encoded MSC classes, and should be used as target variables of the multi-label classification task. |
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- Other columns are auxiliary: |
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- `url`) the URL of the preprint (the latest version as of December 2023), |
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- `title`) the original title, |
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- `abstract`) the original abstract, |
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- `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). |
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- **Subtask**) Predicting `primary_category` based on `cleaned_text`, a multi-class text classification task with ~30 distinct labels. |
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## Data Splits |
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Stratified sampling was used for splitting the data so that the proportions of a target variable among the splits are not very different. |
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|Dataset |Description |Number of instances | |
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|---------|------------------|----------------------| |
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|main.zip |the whole data |164,230 | |
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|train.zip|the training set |104,675 | |
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|val.zip |the validation set|18,540 | |
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|test.zip |the test set |41,015 | |
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## Data Collection and Cleaning |
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The details are outlined in this [notebook](https://github.com/FilomKhash/Math-Preprint-Classifier/blob/main/Scarping%20and%20Cleaning%20the%20Data.ipynb). |
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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: |
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- dropping preprints with an abnormally high number of versions, |
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- keeping only the last arXiv version, |
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- dropping preprints whose metadata does not include any MSC class, |
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- dropping entries with pre-2010 mathematics subject classification convention, |
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- concatenating abstract and title strings and carrying out the following steps to obtain the `cleaned_text` column: |
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- removing the LaTeX math environment and URL citations, |
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- make the text lower case, normalizing accents and removing special characters, |
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- removing English and some corpus-specific stop words, |
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- stemming. |
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## Citation |
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<https://github.com/FilomKhash/Math-Preprint-Classifier> |
