https://github.com/xiaohan2012/multi-label-text-classification
Mutli-label text classification using ConvNet and graph embedding (Tensorflow implementation)
https://github.com/xiaohan2012/multi-label-text-classification
convnet graph-embedding multi-label-classification tensorflow
Last synced: 3 months ago
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Mutli-label text classification using ConvNet and graph embedding (Tensorflow implementation)
- Host: GitHub
- URL: https://github.com/xiaohan2012/multi-label-text-classification
- Owner: xiaohan2012
- Created: 2017-08-14T20:31:26.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T01:53:20.000Z (over 2 years ago)
- Last Synced: 2024-04-14T18:06:58.870Z (about 1 year ago)
- Topics: convnet, graph-embedding, multi-label-classification, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 776 KB
- Stars: 45
- Watchers: 1
- Forks: 15
- Open Issues: 22
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Metadata Files:
- Readme: README.md
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README
# graph embedding + deep learning for multi-label text classification
this projects attempts to combine:
- **graph embedding**
- **ConvNet**
for the purpose of **multi-label text classification**.
I compared three methods on stackexchange datasets, where the goal is to predict the tags of posts.
If you wan to know more, here are [some slides](https://github.com/xiaohan2012/network_embedding/blob/master/project-slides.pdf)
# utility scripts
- `scripts/preprocessing_pipeline.sh`: all the preprocessing, data splitting, feature extractio, etc
- `sample_random_walks.py`: sample random walks on a graph
- `extract_embedding_labels.py`: extract labels for embedding visualization
# main scripts
- `fastxml_experiment.py`: experiment for fastxml
- `kim_cnn_experiment.py`: experiment for cnn
- `combined_model_experiment.py`: experiment for cnn + deepwalk