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.
- Python 3.6+
- Tensorflow/Tensorflow-gpu 1.12.0
- Scipy 1.5.1
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.
@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}
}