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
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
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent