Graph Convolutional Networks for Text Classification in PyTorch

Implementation of Graph Convolutional Networks for Text Classification [1].

Tested on the 20NG/R8/R52/Ohsumed/MR data set, the code on this repository can achieve the effect of the paper.

Benchmark

dataset	20NG	R8	R52	Ohsumed	MR
yao8839836 (official)	0.8634	0.9707	0.9356	0.6836	0.7674
chengsen	0.8618	0.9704	0.9354	0.6827	0.7643
this repo	0.000	0.000	0.000	0.000	0.000

NOTE: The result of the experiment is to repeat the run 1.

Adding dataset kontan1: Multiclass classification (5 class), news in Indonesia. Accuracy: 0.000

Adding dataset kontan2: Sentiment classification, news in Indonesia. Accuracy: 0.000

Requirements

• python==3.8.13
• fastai==2.0.15
• torch==1.10.2
• scipy==1.5.2
• pandas==1.3.5
• spacy==2.3.1
• nltk==3.7
• prettytable==1.0.0
• numpy==1.21.5
• networkx==2.6.3
• tqdm==4.62.3
• scikit_learn==1.1.2
• matplotlib==3.5.2

Usage

1. Process the data first, run data_processor.py
2. Generate graph, run build_graph.py
3. Training model, run trainer.py

add -h for help on each usage command.

References

[1] Yao, L. , Mao, C. , & Luo, Y. . (2018). Graph convolutional networks for text classification.