Understanding Graph Convolutional Networks for Text Classification

Official Implementation for AAAI 2022 on DLG (https://arxiv.org/abs/2203.16060)

Han, C.*, Yuan, Z.*, Wang, K., Long, S., & Poon, J. (2022).
Understanding Graph Convolutional Networks for Text Classification
In proceeding of AAAI 2022 on DLG

*co first author

Easy running using .ipynb file

You can simply run the code with your data using final.ipynb, remember to fill in your dataset into a list of documents/labels

original_train_sentences = 
original_labels_train = 
original_test_sentences = 
original_labels_test = 

# example 
# original_train_sentences = ['this is sample 1','this is sample 2']
# original_labels_train = ['postive','negative']
# original_test_sentences = ['this is sample 1','this is sample 2']
# original_labels_test = ['postive','negative']

Also, some other parameters can be modified

# EDGE: 0 means only d2w edge, 1 means d2w+w2w, 2 means d2w+w2w+d2d edge
EDGE = 0

# NODE: 0 means one-hot as input, 1 means BERT embedding as input
NODE = 0 

NUM_LAYERS = 2 
HIDDEN_DIM = 200
DROP_OUT = 0.5
LR = 0.02
WEIGHT_DECAY = 0
EARLY_STOPPING = 10
NUM_EPOCHS = 200