TextING

The code and dataset for the ACL2020 paper Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks, implemented in Tensorflow.

Some functions are based on Text GCN. Thank for their work.

Requirements

• Python 3.6+
• Tensorflow/Tensorflow-gpu 1.12.0
• Scipy 1.5.1

Usage

Download pre-trained word embeddings glove.6B.300d.txt from here and unzip to the repository.

Build graphs from the datasets in data/corpus/ as:

python build_graph.py [DATASET] [WINSIZE]

Provided datasets include mr,ohsumed,R8andR52. The default sliding window size is 3.

To use your own dataset, put the text file under data/corpus/ and the label file under data/ as other datasets do. Preprocess the text by running remove_words.py before building the graphs.

Start training and inference as:

python train.py [--dataset DATASET] [--learning_rate LR]
                [--epochs EPOCHS] [--batch_size BATCHSIZE]
                [--hidden HIDDEN] [--steps STEPS]
                [--dropout DROPOUT] [--weight_decay WD]

To reproduce the result, large hidden size and batch size are suggested as long as your memory allows. We report our result based on 96 hidden size with 1 batch. For the sake of memory efficiency, you may change according to your hardware.

Citation

@inproceedings{zhang2020every,
  title={Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks},
  author={Zhang, Yufeng and Yu, Xueli and Cui, Zeyu and Wu, Shu and Wen, Zhongzhen and Wang, Liang},
  booktitle="Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
  year={2020}
}