# text_gcn
The implementation of Text GCN in our paper:
Liang Yao, Chengsheng Mao, Yuan Luo. "Graph Convolutional Networks for Text Classification." In 33rd AAAI Conference on Artificial Intelligence (AAAI-19), 7370-7377
## Require
Python 2.7 or 3.6
Tensorflow >= 1.4.0
## Reproducing Results
1. Run `python remove_words.py 20ng`
2. Run `python build_graph.py 20ng`
3. Run `python train.py 20ng`
4. Change `20ng` in above 3 command lines to `R8`, `R52`, `ohsumed` and `mr` when producing results for other datasets.
## Example input data
1. `/data/20ng.txt` indicates document names, training/test split, document labels. Each line is for a document.
2. `/data/corpus/20ng.txt` contains raw text of each document, each line is for the corresponding line in `/data/20ng.txt`
3. `prepare_data.py` is an example for preparing your own data, note that '\n' is removed in your documents or sentences.
## Inductive version
An inductive version of Text GCN is [fast_text_gcn](https://github.com/yao8839836/fast_text_gcn), where test documents are not included in training process.
text_gcn：用于文本分类的图卷积网络。 AAAI 2019
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2021-02-03
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text_gcn：用于文本分类的图卷积网络。 AAAI 2019 （26397个子文件） 
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