Summary of Text Classification Using Graph Convolutional Networks: a Comprehensive Survey, by Syed Mustafa Haider Rizvi et al.
Text Classification using Graph Convolutional Networks: A Comprehensive Survey
by Syed Mustafa Haider Rizvi, Ramsha Imran, Arif Mahmood
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive survey of Graph Convolution Network (GCN)-based approaches for text classification, a fundamental problem in Natural Language Processing with applications in sentiment analysis, fake news detection, medical diagnosis, and document classification. The authors identify strengths and limitations of various GCN-based architectures and modes of supervision, comparing their performance on benchmark datasets. The paper aims to provide an updated overview of the state-of-the-art techniques and highlight future research directions and challenges in this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can understand text messages better. It’s important because we use computers to analyze emotions, detect fake news, diagnose medical conditions, and organize documents. Lots of researchers have tried different ways to make computers do this well, but some methods are better than others. The authors look at one type of approach called Graph Convolution Networks (GCNs) that has been very successful in recent years. They see what works well and what doesn’t, comparing different approaches on standard tests. The paper also talks about where we go from here and the challenges that remain. |
Keywords
» Artificial intelligence » Classification » Gcn » Natural language processing » Text classification
