Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification

Sun, Zhongtian and Harit, Anoushka and Cristea, Alexandra I. and Yu, Jialin and Shi, Lei and Al Moubayed, Noura (2022) 'Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.', The 2022 International Joint Conference on Neural Networks (IJCNN) Padova, Italy, 18-23 July 2022.

Abstract

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the vanilla Graph Convolution Network (GCN), which requires a large amount of labelled data during training and thus is less robust when dealing with large-scale graph data with fewer labels. Additionally, graph structure information is normally captured by direct information aggregation via network schema and is highly dependent on correct adjacency information. Therefore, any missing adjacency knowledge may hinder the performance. Addressing these problems, this paper thus proposes a novel method to learn a graph structure, NC-HGAT, by expanding a state-of-the-art self-supervised heterogeneous graph neural network model (HGAT) with simple neighbour contrastive learning. The new NC-HGAT considers the graph structure information from heterogeneous graphs with multilayer perceptrons (MLPs) and delivers consistent results, despite the corrupted neighbouring connections. Extensive experiments have been implemented on four benchmark short-text datasets. The results demonstrate that our proposed model NC-HGAT significantly outperforms state-of-the-art methods on three datasets and achieves competitive performance on the remaining dataset.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
Download PDF
(385Kb)
Status:Peer-reviewed
Publisher Web site:https://ieeexplore.ieee.org/xpl/conhome/1000500/all-proceedings
Publisher statement:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:26 April 2022
Date deposited:01 September 2022
Date of first online publication:2022
Date first made open access:01 September 2022

Save or Share this output

Export:
Export
Look up in GoogleScholar