Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, the proposed models do not incorporate the knowledge of the document structure in the architecture efficiently and not take into account the contexting importance of words and sentences. In this paper, we propose a new approach based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms for document classification tasks. The main contribution of this work is the use of convolution layers to extract more meaningful, generalizable and abstract features by the hierarchical representation. The proposed method in this paper improves the results of the current attention-based approaches for document classification.
@article{arxiv.1901.06610,
title = {Hierarchical Attentional Hybrid Neural Networks for Document Classification},
author = {Jader Abreu and Luis Fred and David Macêdo and Cleber Zanchettin},
journal= {arXiv preprint arXiv:1901.06610},
year = {2019}
}
Comments
Paper accepted at International Conference on Artificial Neural Networks - ICANN 2019