# Hierarchical Attentional Hybrid Neural Networks for Document Classification

## TL;DR

This paper introduces a hierarchical neural network model combining convolutional layers, gated recurrent units, and attention mechanisms to improve document classification by better capturing document structure and contextual importance.

## Contribution

It presents a novel hierarchical model that effectively incorporates document structure and context, outperforming existing attention-based methods.

## Key findings

- Improved classification accuracy over existing models
- Effective hierarchical feature extraction
- Enhanced understanding of document structure

## Abstract

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.