综述图卷积网络在文本分类中的应用与进展
Text Classification using Graph Convolutional Networks: A Comprehensive Survey
- 按架构与监督方式分类，梳理GCN文本分类方法
- 对比多种方法在多个基准数据集上的表现
- 适合关注NLP中图神经网络应用的研究者
文本分类是自然语言处理中的核心且实用的问题，广泛应用于情感分析、虚假新闻检测、医学诊断和文档分类等领域。近年来，众多研究从不同角度探索文本分类，取得了不同程度的成功。基于图卷积网络（GCN）的方法在过去十年中备受关注，许多实现已在近期文献中达到或接近最优性能，因此亟需一份更新的综述。本文旨在总结并分类各种基于GCN的文本分类方法，涵盖其架构设计与监督模式，分析其优势与局限，并在多个基准数据集上比较性能。同时讨论该领域未来的研究方向与现存挑战。
原文摘要 · Abstract (English)
Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recent works exists where researchers have studied and tackled text classification from different angles with varying degrees of success. Graph convolution network (GCN)-based approaches have gained a lot of traction in this domain over the last decade with many implementations achieving state-of-the-art performance in more recent literature and thus, warranting the need for an updated survey. This work aims to summarize and categorize various GCN-based Text Classification approaches with regard to the architecture and mode of supervision. It identifies their strengths and limitations and compares their performance on various benchmark datasets. We also discuss future research directions and the challenges that exist in this domain.
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