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arXiv · 2203.16060

Understanding Graph Convolutional Networks for Text Classification

Soyeon Caren Han·Zihan Yuan·Kunze Wang·Siqu Long·Josiah Poon
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Abstract

Graph Convolutional Networks (GCN) have been effective at tasks that have rich relational structure and can preserve global structure information of a dataset in graph embeddings. Recently, many researchers focused on examining whether GCNs could handle different Natural Language Processing tasks, especially text classification. While applying GCNs to text classification is well-studied, its graph construction techniques, such as node/edge selection and their feature representation, and the optimal GCN learning mechanism in text classification is rather neglected. In this paper, we conduct a comprehensive analysis of the role of node and edge embeddings in a graph and its GCN learning techniques in text classification. Our analysis is the first of its kind and provides useful insights into the importance of each graph node/edge construction mechanism when applied at the GCN training/testing in different text classification benchmarks, as well as under its semi-supervised environment.

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Soyeon Caren Han, Zihan Yuan, Kunze Wang, Siqu Long, Josiah Poon. 2022-03-30. Understanding Graph Convolutional Networks for Text Classification. https://arxiv.org/abs/2203.16060

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