# Weakly-Supervised Neural Text Classification

arXiv: https://arxiv.org/abs/1809.01478 · HTML: https://mlsanity.com/paper/1809.01478/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

## Built on by (most cited first, 30 of 66)

- [Generating Training Data with Language Models: Towards Zero-Shot Language Understanding](https://mlsanity.com/paper/2202.04538/) (2022)
- [CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://mlsanity.com/paper/2310.15638/) (2023)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [Theoretical Analysis of Weak-to-Strong Generalization](https://mlsanity.com/paper/2405.16043/) (2024)
- [Out-of-Scope Intent Detection with Self-Supervision and Discriminative Training](https://mlsanity.com/paper/2106.08616/) (2021)
- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning](https://mlsanity.com/paper/2211.03044/) (2022)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [The Rich Get Richer: Disparate Impact of Semi-Supervised Learning](https://mlsanity.com/paper/2110.06282/) (2021)
- [Weakly-supervised Text Classification Based on Keyword Graph](https://mlsanity.com/paper/2110.02591/) (2021)
- [TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision](https://mlsanity.com/paper/2403.00165/) (2024)
- [Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data](https://mlsanity.com/paper/2106.08977/) (2021)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [WeDef: Weakly Supervised Backdoor Defense for Text Classification](https://mlsanity.com/paper/2205.11803/) (2022)
- [Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data](https://mlsanity.com/paper/2109.10856/) (2021)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks](https://mlsanity.com/paper/2102.11479/) (2021)
- [LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification](https://mlsanity.com/paper/2205.12528/) (2022)
- [Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations](https://mlsanity.com/paper/2210.16637/) (2022)
- [Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification](https://mlsanity.com/paper/2202.05932/) (2022)
- [ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification](https://mlsanity.com/paper/2204.04618/) (2022)
- [Weakly Supervised Text Classification using Supervision Signals from a Language Model](https://mlsanity.com/paper/2205.06604/) (2022)
- [MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information](https://mlsanity.com/paper/2111.04022/) (2021)
- [Resolving label uncertainty with implicit posterior models](https://mlsanity.com/paper/2202.14000/) (2022)
- [LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction](https://mlsanity.com/paper/2208.10833/) (2022)
- [Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training](https://mlsanity.com/paper/2306.07193/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)

## Abstract (arXiv)

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.