# Weakly-supervised Text Classification Based on Keyword Graph

arXiv: https://arxiv.org/abs/2110.02591 · HTML: https://mlsanity.com/paper/2110.02591/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning](https://mlsanity.com/paper/2009.10273/) (2020)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [X-Class: Text Classification with Extremely Weak Supervision](https://mlsanity.com/paper/2010.12794/) (2020)
- [Weakly-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/1812.11270/) (2018)
- [Learning with Weak Supervision for Email Intent Detection](https://mlsanity.com/paper/2005.13084/) (2020)
- [Multilingual Hierarchical Attention Networks for Document Classification](https://mlsanity.com/paper/1707.00896/) (2017)

## Built on by (most cited first, 25 of 25)

- [Zero-Shot Text Classification with Self-Training](https://mlsanity.com/paper/2210.17541/) (2022)
- [TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision](https://mlsanity.com/paper/2403.00165/) (2024)
- [DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples](https://mlsanity.com/paper/2110.13740/) (2021)
- [The ACL OCL Corpus: Advancing Open Science in Computational Linguistics](https://mlsanity.com/paper/2305.14996/) (2023)
- [Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM](https://mlsanity.com/paper/2304.11872/) (2023)
- [Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations](https://mlsanity.com/paper/2210.16637/) (2022)
- [LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction](https://mlsanity.com/paper/2208.10833/) (2022)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification](https://mlsanity.com/paper/2204.11205/) (2022)
- [A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches](https://mlsanity.com/paper/2305.12749/) (2023)
- [PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training](https://mlsanity.com/paper/2305.13723/) (2023)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [MEGClass: Extremely Weakly Supervised Text Classification via Mutually-Enhancing Text Granularities](https://mlsanity.com/paper/2304.01969/) (2023)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [Less than One-shot: Named Entity Recognition via Extremely Weak Supervision](https://mlsanity.com/paper/2311.02861/) (2023)
- [Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification](https://mlsanity.com/paper/2305.14794/) (2023)
- [XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision](https://mlsanity.com/paper/2311.00189/) (2023)
- [CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels](https://mlsanity.com/paper/2306.02693/) (2023)
- [RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules](https://mlsanity.com/paper/2403.02932/) (2024)
- [Are We Really Making Much Progress in Text Classification? A Comparative Review](https://mlsanity.com/paper/2204.03954/) (2022)
- [Low-resource Personal Attribute Prediction from Conversation](https://mlsanity.com/paper/2211.15324/) (2022)
- [FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification](https://mlsanity.com/paper/2212.05506/) (2022)
- [Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures](https://mlsanity.com/paper/2308.06199/) (2023)
- [Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification](https://mlsanity.com/paper/2309.13543/) (2023)
- [CrossTune: Black-Box Few-Shot Classification with Label Enhancement](https://mlsanity.com/paper/2403.12468/) (2024)

## Abstract (arXiv)

Weakly-supervised text classification has received much attention in recent years for it can alleviate the heavy burden of annotating massive data. Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudo-labels for unlabeled texts. However, existing methods treat keywords independently, thus ignore the correlation among them, which should be useful if properly exploited. In this paper, we propose a novel framework called ClassKG to explore keyword-keyword correlation on keyword graph by GNN. Our framework is an iterative process. In each iteration, we first construct a keyword graph, so the task of assigning pseudo labels is transformed to annotating keyword subgraphs. To improve the annotation quality, we introduce a self-supervised task to pretrain a subgraph annotator, and then finetune it. With the pseudo labels generated by the subgraph annotator, we then train a text classifier to classify the unlabeled texts. Finally, we re-extract keywords from the classified texts. Extensive experiments on both long-text and short-text datasets show that our method substantially outperforms the existing ones