Graph Convolutional Networks for Text Classification. AAAI 2019
Graph Convolutional Networks for Text Classification. AAAI 2019
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
Python 2.7 or 3.6
Tensorflow >= 1.4.0
Run python remove_words.py 20ng
Run python build_graph.py 20ng
Run python train.py 20ng
Change 20ng in above 3 command lines to R8, R52, ohsumed and mr when producing results for other datasets.
/data/20ng.txt indicates document names, training/test split, document labels. Each line is for a document.
/data/corpus/20ng.txt contains raw text of each document, each line is for the corresponding line in /data/20ng.txt
prepare_data.py is an example for preparing your own data, note that '\n' is removed in your documents or sentences.
An inductive version of Text GCN is fast_text_gcn, where test documents are not included in training process.
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