Text classification optimization algorithm based on graph neural network

TL;DR

This paper presents an optimized graph neural network-based algorithm for text classification that improves accuracy and efficiency by adaptive graph construction and efficient convolution, outperforming traditional and existing GNN methods.

Contribution

The paper introduces a novel GNN-based text classification algorithm with adaptive graph construction and efficient convolution, enhancing performance and reducing training costs.

Findings

Outperforms traditional text classification methods.

Achieves higher accuracy on multiple datasets.

Reduces computational costs of GNN training.

Abstract

In the field of natural language processing, text classification, as a basic task, has important research value and application prospects. Traditional text classification methods usually rely on feature representations such as the bag of words model or TF-IDF, which overlook the semantic connections between words and make it challenging to grasp the deep structural details of the text. Recently, GNNs have proven to be a valuable asset for text classification tasks, thanks to their capability to handle non-Euclidean data efficiently. However, the existing text classification methods based on GNN still face challenges such as complex graph structure construction and high cost of model training. This paper introduces a text classification optimization algorithm utilizing graph neural networks. By introducing adaptive graph construction strategy and efficient graph convolution operation, the accuracy and efficiency of text classification are effectively improved. The experimental results demonstrate that the proposed method surpasses traditional approaches and existing GNN models across multiple public datasets, highlighting its superior performance and feasibility for text classification tasks.