# Prompt-Learning for Short Text Classification arXiv: https://arxiv.org/abs/2202.11345 ยท HTML: https://mlsanity.com/paper/2202.11345/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 64 references that are in the map. - First posted on arXiv 23 Feb 2022. ## Builds on (most cited first, 30 of 32) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021) - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021) - [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015) - [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021) - [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019) - [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019) - [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020) - [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014) - [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020) - [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020) - [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020) - [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019) - [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021) - [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021) - [WARP: Word-level Adversarial ReProgramming](https://mlsanity.com/paper/2101.00121/) (2021) - [Assessing BERT's Syntactic Abilities](https://mlsanity.com/paper/1901.05287/) (2019) - [Commonsense Knowledge Mining from Pretrained Models](https://mlsanity.com/paper/1909.00505/) (2019) - [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021) - [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021) - [Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://mlsanity.com/paper/2010.13641/) (2020) - [Prompt-Learning for Fine-Grained Entity Typing](https://mlsanity.com/paper/2108.10604/) (2021) - [Prototypical Representation Learning for Relation Extraction](https://mlsanity.com/paper/2103.11647/) (2021) - [Acquiring Knowledge from Pre-trained Model to Neural Machine Translation](https://mlsanity.com/paper/1912.01774/) (2019) - [Universal Text Representation from BERT: An Empirical Study](https://mlsanity.com/paper/1910.07973/) (2019) - [Deep Short Text Classification with Knowledge Powered Attention](https://mlsanity.com/paper/1902.08050/) (2019) - [Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering](https://mlsanity.com/paper/1904.06652/) (2019) - [Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer](https://mlsanity.com/paper/2201.05411/) (2022) ## Built on by (most cited first, 11 of 11) - [Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs](https://mlsanity.com/paper/2311.15317/) (2023) - [CoPL: Contextual Prompt Learning for Vision-Language Understanding](https://mlsanity.com/paper/2307.00910/) (2023) - [Similarity-Aware Multimodal Prompt Learning for Fake News Detection](https://mlsanity.com/paper/2304.04187/) (2023) - [SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection](https://mlsanity.com/paper/2404.09481/) (2024) - [Are We Really Making Much Progress in Text Classification? A Comparative Review](https://mlsanity.com/paper/2204.03954/) (2022) - [A multi-model-based deep learning framework for short text multiclass classification with the imbalanced and extremely small data set](https://mlsanity.com/paper/2206.12027/) (2022) - [LabelPrompt: Effective Prompt-based Learning for Relation Classification](https://mlsanity.com/paper/2302.08068/) (2023) - [Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting](https://mlsanity.com/paper/2502.14897/) (2025) - [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025) - [Small sample-based adaptive text classification through iterative and contrastive description refinement](https://mlsanity.com/paper/2508.00957/) (2025) - [Automated Generation of Research Workflows from Academic Papers: A Full-text Mining Framework](https://mlsanity.com/paper/2509.12955/) (2025) ## Abstract (arXiv) In the short text, the extremely short length, feature sparsity, and high ambiguity pose huge challenges to classification tasks. Recently, as an effective method for tuning Pre-trained Language Models for specific downstream tasks, prompt-learning has attracted a vast amount of attention and research. The main intuition behind the prompt-learning is to insert the template into the input and convert the text classification tasks into equivalent cloze-style tasks. However, most prompt-learning methods expand label words manually or only consider the class name for knowledge incorporating in cloze-style prediction, which will inevitably incur omissions and bias in short text classification tasks. In this paper, we propose a simple short text classification approach that makes use of prompt-learning based on knowledgeable expansion. Taking the special characteristics of short text into consideration, the method can consider both the short text itself and class name during expanding label words space. Specifically, the top N concepts related to the entity in the short text are retrieved from the open Knowledge Graph like Probase, and we further refine the expanded label words by the distance calculation between selected concepts and class labels. Experimental results show that our approach obtains obvious improvement compared with other fine-tuning, prompt-learning, and knowledgeable prompt-tuning methods, outperforming the state-of-the-art by up to 6 Accuracy points on three well-known datasets.