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liuxinchen3 725720a11c | 1 year ago | |
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data | 1 year ago | |
models | 1 year ago | |
preprocess | 1 year ago | |
src | 1 year ago | |
utils | 1 year ago | |
.DS_Store | 1 year ago | |
README.md | 1 year ago | |
config.py | 1 year ago | |
train.py | 1 year ago | |
tsne.py | 1 year ago |
This implementation highly based on official code yao8839836/text_gcn.
cd ./preprocess
python remove_words.py <dataset>
python build_graph.py <dataset>
cd ..
python train.py <dataset>
<dataset>
with 20ng
, R8
, R52
, ohsumed
or mr
Pre-tained model placed in ‘model_save’.
This work was supported by the National Key R&D Program of China under Grant No. 2020AAA0103804(Sponsor: Hefu Liu). This work belongs to the University of science and technology of China.
本算法提出了一种新颖的基于图神经网络的的文本分类算法,TextGCAT。具体pipeline是:将文档出现的关键词视作图中的点,根据关键词是否出现在同一句话的共现概率构造联系,也就是图中的边,然后利用图神经网络提取关键词之间的依赖关系进而对文本进行分类。
Text Python
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