README finalized (v1).

README.md

@@ -12,29 +12,50 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: "train.zip"
+    path: train.zip
   - split: validation
-    path: "val.zip"
+    path: val.zip
   - split: test
-    path: "test.zip"
+    path: test.zip
+pretty_name: MSC
 ---
 ## Dataset Description
-The text data (title and abstract) of 164230 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.
+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-lable classification task.
-- Other columns are auxiliary and contain URLs, the original titles and abstracts, and the primary arXiv category.
+- 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    |164230               |
-|train.zip|the training set  |104675               |
-|val.zip  |the validation set|18540                |
-|test.zip |the test set      |41015                |
+|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
+## 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>