Text Classification using Graph Convolutional Networks: A Comprehensive Survey
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 75 of its 183 references that are in the map. First posted on arXiv 12 Oct 2024.
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References · 75
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
38 more
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
- 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
- 2014DeepWalk: Online Learning of Social Representationscited by 1,547
- 2019Simplifying Graph Convolutional Networkscited by 1,477
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
- 2016Variational Graph Auto-Encoderscited by 1,382
- 2018Attention-based Deep Multiple Instance Learningcited by 1,095
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
- 2018Neural Network Acceptability Judgmentscited by 958
- 2016Bag of Tricks for Efficient Text Classificationcited by 876
- 2015Gated Graph Sequence Neural Networkscited by 796
- 2014Distributed Representations of Sentences and Documentscited by 678
- 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
- 2019Graph Transformer Networkscited by 523
- 2015An Introduction to Convolutional Neural Networkscited by 514
- 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
- 2019Diffusion Improves Graph Learningcited by 414
- 2019How to Fine-Tune BERT for Text Classification?cited by 411
- 2018Graph Convolutional Networks for Text Classificationcited by 345
- 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
- 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
- 2019Text Classification Algorithms: A Surveycited by 163
- 2016Recurrent Neural Network for Text Classification with Multi-Task Learningcited by 144
- 2020Deep Learning Based Text Classification: A Comprehensive Reviewcited by 141
- 2022Data Augmentation for Deep Graph Learning: A Surveycited by 133
- 2019Augmenting Data with Mixup for Sentence Classification: An Empirical Studycited by 125
- 2021Entailment as Few-Shot Learnercited by 123
- 2021Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learnerscited by 115
- 2020Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networkscited by 77
- 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
- 2018Joint Embedding of Words and Labels for Text Classificationcited by 69
- 2019DocBERT: BERT for Document Classificationcited by 68
- 2021LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classificationcited by 61
- 2019Text Level Graph Neural Network for Text Classificationcited by 60
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