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https://github.com/luoyuanlab/text_gcn_tutorial
A tutorial & minimal example (8min on CPU) for Graph Convolutional Networks for Text Classification. AAAI 2019
https://github.com/luoyuanlab/text_gcn_tutorial
deep-learning graph-convolutional-networks nlp text-classification
Last synced: about 2 months ago
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A tutorial & minimal example (8min on CPU) for Graph Convolutional Networks for Text Classification. AAAI 2019
- Host: GitHub
- URL: https://github.com/luoyuanlab/text_gcn_tutorial
- Owner: luoyuanlab
- Created: 2019-03-24T23:36:32.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-04T15:40:44.000Z (almost 4 years ago)
- Last Synced: 2024-05-21T15:17:58.831Z (4 months ago)
- Topics: deep-learning, graph-convolutional-networks, nlp, text-classification
- Language: Python
- Homepage:
- Size: 4.63 MB
- Stars: 26
- Watchers: 1
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-gcn - yuanluo/text_gcn_tutorial - 19), (Text Classification:)
README
# Text GCN Tutorial
This tutorial (currently under improvement) is based on 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)
# Require
Python 2.7 or 3.6
Tensorflow >= 1.4.0
# Example input data
The Ohsumed corpus is from the MEDLINE database, which is a bibliographic database of important medical literature maintained by the National Library of Medicine
In this tutorial, we created a subsample of the 2,762 unique diseases abstracts from 3 categories
* C04: Neoplasms
* C10: Nervous System Diseases
* C14: Cardiovascular Diseases
As we focus on single-label text classification, the documents belonging to multiple categories are excluded
1230 train (use 10% as validation), 1532 test
1. `/data/ohsumed_3.txt` indicates document names, training/test split, document labels. Each line is for a document.
2. `/data/corpus/ohsumed_3.txt` contains raw text of each document, each line is for the corresponding line in `/data/ohsumed_3.txt`
# Reproduing Results
1. Run `python remove_words.py ohsumed_3`
2. Run `python build_graph.py ohsumed_3`
3. Run `python train.py ohsumed_3`
# Example output
```
2019-04-04 22:58:26.244395: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Epoch: 0001 train_loss= 1.09856 train_acc= 0.41463 val_loss= 1.08209 val_acc= 0.48780 time= 29.13731
Epoch: 0002 train_loss= 1.08044 train_acc= 0.49865 val_loss= 1.05469 val_acc= 0.47967 time= 23.00088
Epoch: 0003 train_loss= 1.05075 train_acc= 0.49865 val_loss= 1.02113 val_acc= 0.47967 time= 21.82401
Epoch: 0004 train_loss= 1.01430 train_acc= 0.49955 val_loss= 0.98582 val_acc= 0.48780 time= 21.42816
Epoch: 0005 train_loss= 0.97174 train_acc= 0.50678 val_loss= 0.95375 val_acc= 0.51220 time= 21.44958
Epoch: 0006 train_loss= 0.93406 train_acc= 0.51220 val_loss= 0.92789 val_acc= 0.55285 time= 24.01502
......
Epoch: 0074 train_loss= 0.01921 train_acc= 0.99819 val_loss= 0.09674 val_acc= 0.96748 time= 24.01229
Epoch: 0075 train_loss= 0.02093 train_acc= 0.99909 val_loss= 0.09715 val_acc= 0.96748 time= 24.08436
Early stopping...
Optimization Finished!
Test set results: cost= 0.24295 accuracy= 0.92167 time= 7.60145
10456
Test Precision, Recall and F1-Score...
precision recall f1-score support
0 0.8882 0.8363 0.8614 342
1 0.9438 0.9517 0.9477 600
2 0.9174 0.9407 0.9289 590
avg / total 0.9212 0.9217 0.9212 1532
```
# Visualizing Documents
Run `python tsne.py`
# Example Visualization
