TextSSL

<h1 align="left">Sparse Structure Learning via Graph Neural Networks for inductive document classification</h1> <p align="left"> <a href="https://arxiv.org/abs/2112.06386"><img src="https://img.shields.io/badge/arXiv-2202.05441-b31b1b.svg" alt="Paper"></a> <a href="https://github.com/qkrdmsghk/TextSSL"><img src="https://img.shields.io/badge/-Github-grey?logo=github" alt="Github"></a> <a href="https://ojs.aaai.org/index.php/AAAI/article/view/21366"> <img alt="License" src="https://img.shields.io/static/v1?label=Pub&message=AAAI%2722&color=blue"> </a> <a href="https://github.com/qkrdmsghk/TextSSL/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/qkrdmsghk/TextSSL?color=blue"> </a> </p> <p align="left"><img src="TextSSL.png" width=80% height=80%></p> <p align="left"><em>Figure 1.</em> The architecture of TextSSL.</p>

About data

We use the same benchmark datasets that are used in Yao, Mao, and Luo 2019, where we follow the same train/test splits and data preprocessing for MR, Ohsumed and 20NG datasets as Kim 2014; Yao, Mao, and Luo 2019. Thanks for their work.

For R8 and R52 datasets, they are only provided by a preprocessed version that lack punctuations and do not have explicit sample names. Since we use documents with sentence segmentation information to construct graph, we re-extract the data from original Reuters-21578 dataset.

You can download the dataset here:

1. re-extract R8 and R52 datasets.

   python re-extract_data/mk_R8_R52.py --name R8
   

2. remove words.

   python remove_words.py --name R8
   

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About path

To run the code, you should change Your_path=/data/project/yinhuapark/ssl/ to your own path.

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Make graph dataset

1. create co-occurrence pairs of each documents.

   python ssl_make_graphs/create_cooc_document.py --name R8 
   

2. construct graphs of each documents in InMemoryDatset.

   python ssl_make_graphs/PygDocsGraphDataset.py --name R8 
   

Train

python ssl_graphmodels/pyg_models/train_docs.py --name R8

Reference

If you find our paper and repo useful, please cite our paper:

@inproceedings{piao2022sparse,
  title={Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification},
  author={Piao, Yinhua and Lee, Sangseon and Lee, Dohoon and Kim, Sun},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={10},
  pages={11165--11173},
  year={2022}
}

The readme is inspired by GSAT.