Hierarchical Attentional Hybrid Neural Networks for Document Classification

This paper was accepted in ICANN 2019

J. Abreu , L. Fred, D. Macêdo, C. Zanchettin, "Hierarchical Attentional Hybrid Neural Networks for Document Classification".

Performance on Yelp Dataset multi-class

Datasets:

Do you want use Pre-trained FastText word embeddings? Downloaded in https://www.kaggle.com/luisfredgs/wiki-news-300d-1m-subword. Check the source code for more details. Pay attention to Colab limits of RAM and GPU.

Requirements

• Python 3
• tensorflow 1.10
• Keras 2.x
• spacy 2.0
• gensim
• tqdm
• matplotlib

A GPU with CUDA support is required to run this code.

Run this code on Google Colab with Free GPU

On Google Colab, Select "Runtime," "Change runtime type" to Python 3. Ensure "Hardware accelerator" is set to GPU (the default is CPU).

To run this notebook on Google Colab you don't need download dataset files. Type your kaggle username and API key during cell execution and wait. Will done. If do you want to make predictions on new text data using a trained model, check make_predictions.ipynb for more details.

Please cite

@article{abreu2019hierarchical,
  title={Hierarchical Attentional Hybrid Neural Networks for Document Classification},
  author={Abreu, Jader and Fred, Luis and Mac{\^e}do, David and Zanchettin, Cleber},
  journal={arXiv preprint arXiv:1901.06610},
  year={2019}
}