# Deep Short Text Classification with Knowledge Powered Attention

arXiv: https://arxiv.org/abs/1902.08050 · HTML: https://mlsanity.com/paper/1902.08050/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 22 of 22)

- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba](https://mlsanity.com/paper/2106.01686/) (2021)
- [Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks](https://mlsanity.com/paper/2102.10934/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification](https://mlsanity.com/paper/2111.00180/) (2021)
- [Prompt-Learning for Short Text Classification](https://mlsanity.com/paper/2202.11345/) (2022)
- [Generative Entity Typing with Curriculum Learning](https://mlsanity.com/paper/2210.02914/) (2022)
- [Harnessing Knowledge and Reasoning for Human-Like Natural Language Generation: A Brief Review](https://mlsanity.com/paper/2212.03747/) (2022)
- [MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models](https://mlsanity.com/paper/2407.12309/) (2024)
- [Does Commonsense help in detecting Sarcasm?](https://mlsanity.com/paper/2109.08588/) (2021)
- [CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model](https://mlsanity.com/paper/2401.03158/) (2024)
- [DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search](https://mlsanity.com/paper/2104.11760/) (2021)
- [Automatic tagging of knowledge points for K12 math problems](https://mlsanity.com/paper/2208.09867/) (2022)
- [Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures](https://mlsanity.com/paper/2308.06199/) (2023)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [A Framework for Fast Polarity Labelling of Massive Data Streams](https://mlsanity.com/paper/2203.12368/) (2022)
- [A Machine Learning Approach to Classifying Construction Cost Documents into the International Construction Measurement Standard](https://mlsanity.com/paper/2211.07705/) (2022)
- [Using Text Classification with a Bayesian Correction for Estimating Overreporting in the Creditor Reporting System on Climate Adaptation Finance](https://mlsanity.com/paper/2211.16947/) (2022)
- [GE-Blender: Graph-Based Knowledge Enhancement for Blender](https://mlsanity.com/paper/2301.12850/) (2023)
- [HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding](https://mlsanity.com/paper/2308.09985/) (2023)
- [A Simple Graph Contrastive Learning Framework for Short Text Classification](https://mlsanity.com/paper/2501.09219/) (2025)
- [Linguistically Informed Graph Model and Semantic Contrastive Learning for Korean Short Text Classification](https://mlsanity.com/paper/2603.03652/) (2026)

## Abstract (arXiv)

Short text classification is one of important tasks in Natural Language Processing (NLP). Unlike paragraphs or documents, short texts are more ambiguous since they have not enough contextual information, which poses a great challenge for classification. In this paper, we retrieve knowledge from external knowledge source to enhance the semantic representation of short texts. We take conceptual information as a kind of knowledge and incorporate it into deep neural networks. For the purpose of measuring the importance of knowledge, we introduce attention mechanisms and propose deep Short Text Classification with Knowledge powered Attention (STCKA). We utilize Concept towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS) attention to acquire the weight of concepts from two aspects. And we classify a short text with the help of conceptual information. Unlike traditional approaches, our model acts like a human being who has intrinsic ability to make decisions based on observation (i.e., training data for machines) and pays more attention to important knowledge. We also conduct extensive experiments on four public datasets for different tasks. The experimental results and case studies show that our model outperforms the state-of-the-art methods, justifying the effectiveness of knowledge powered attention.