TGCN: A Novel Deep Learning Model for Text Classification

This project contains

• Re-implementation of "Graph Convolutional Networks for Text Classification" in tensorflow 2.1.
• Some baseline models mentioned in original paper.
• Code is highly based on official repository.
  • Preprocess part is modified from official repository.
  • Model part is written by ourseleves.

Requirement

• python 3.6
• tensorflow 2.1.0
• nltk 3.4.5
• fasttext 0.9.2 (Optional)

Run

Preprocess

cd ./preprocess
python remove_words.py <dataset>
python build_graph.py <dataset>

The selections of <dataset> are R8, R52, 20NG, ohsumed, THUCTC, CHINESE.

Training

python train.py  <dataset>

The selections of <dataset> are R8, R52, 20NG, ohsumed, THUCTC, CHINESE.

Visualization

cd visual
python tsne.py <dataset> <length>

The selections of <dataset> are R8, R52, 20NG, ohsumed, THUCTC, CHINESE.

The selections of <length> are 1, 2.

Data

R8 is provided in cleaned_data dictionary. Other datasets can be downloaded at Google drive.

Results

Accuracy

Embeddings

R8 embeddings in first layer:

R8 embeddings in second layer:

More images can be found at visual dictionary.

Reference

• The official implementation: https://github.com/yao8839836/text_gcn
• PyTorch version: https://github.com/iworldtong/text_gcn.pytorch
• Paper: https://arxiv.org/abs/1809.05679

Contributors:

• Yuan Li
• Dongzi Qu