Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification

TL;DR

This paper introduces ClusterText, a novel graph contrastive learning method that refines negative sampling through clustering and self-correction, significantly improving semi-supervised text classification performance.

Contribution

It proposes a cluster-refined negative sampling strategy combined with a self-correction mechanism to address over-clustering in GCL-based text classification.

Findings

Outperforms existing methods in text classification accuracy

Effectively mitigates negative sampling bias and over-clustering

Demonstrates scalability to large datasets

Abstract

Graph contrastive learning (GCL) has been widely applied to text classification tasks due to its ability to generate self-supervised signals from unlabeled data, thus facilitating model training. However, existing GCL-based text classification methods often suffer from negative sampling bias, where similar nodes are incorrectly paired as negative pairs. This can lead to over-clustering, where instances of the same class are divided into different clusters. To address the over-clustering issue, we propose an innovative GCL-based method of graph contrastive learning via cluster-refined negative sampling for semi-supervised text classification, namely ClusterText. Firstly, we combine the pre-trained model Bert with graph neural networks to learn text representations. Secondly, we introduce a clustering refinement strategy, which clusters the learned text representations to obtain pseudo labels. For each text node, its negative sample set is drawn from different clusters. Additionally, we propose a self-correction mechanism to mitigate the loss of true negative samples caused by clustering inconsistency. By calculating the Euclidean distance between each text node and other nodes within the same cluster, distant nodes are still selected as negative samples. Our proposed ClusterText demonstrates good scalable computing, as it can effectively extract important information from from a large amount of data. Experimental results demonstrate the superiority of ClusterText in text classification tasks.