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[AAAI 2022] Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification

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Sparse Structure Learning via Graph Neural Networks for inductive document classification

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Figure 1. The architecture of TextSSL.

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
    

About path

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


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.

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