# Integrating Semantic Knowledge to Tackle Zero-shot Text Classification

arXiv: https://arxiv.org/abs/1903.12626 · HTML: https://mlsanity.com/paper/1903.12626/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 28 of 28)

- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://mlsanity.com/paper/2108.02035/) (2021)
- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://mlsanity.com/paper/2211.16285/) (2022)
- [Text Clustering with Large Language Model Embeddings](https://mlsanity.com/paper/2403.15112/) (2024)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [Pre-trained Language Models Can be Fully Zero-Shot Learners](https://mlsanity.com/paper/2212.06950/) (2022)
- [MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction](https://mlsanity.com/paper/2109.04108/) (2021)
- [Weakly Supervised Text Classification using Supervision Signals from a Language Model](https://mlsanity.com/paper/2205.06604/) (2022)
- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation](https://mlsanity.com/paper/2112.04539/) (2021)
- [ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling](https://mlsanity.com/paper/2201.01337/) (2022)
- [Benchmarking Knowledge-driven Zero-shot Learning](https://mlsanity.com/paper/2106.15047/) (2021)
- [A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism](https://mlsanity.com/paper/2206.02179/) (2022)
- [Zero-Shot Multi-Label Topic Inference with Sentence Encoders](https://mlsanity.com/paper/2304.07382/) (2023)
- [The Benefits of Label-Description Training for Zero-Shot Text Classification](https://mlsanity.com/paper/2305.02239/) (2023)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach](https://mlsanity.com/paper/2402.03043/) (2024)
- [Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization](https://mlsanity.com/paper/2408.03149/) (2024)
- [Towards Open-Domain Topic Classification](https://mlsanity.com/paper/2306.17290/) (2023)
- [Zero-Shot Multi-Label Classification of Bangla Documents: Large Decoders Vs. Classic Encoders](https://mlsanity.com/paper/2503.02993/) (2025)
- [FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification](https://mlsanity.com/paper/2212.05506/) (2022)
- [Recognizing Unseen States of Unknown Objects by Leveraging Knowledge Graphs](https://mlsanity.com/paper/2307.12179/) (2023)
- [TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification](https://mlsanity.com/paper/2401.13219/) (2024)
- [X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification](https://mlsanity.com/paper/2403.03863/) (2024)
- [A Symmetric Loss Perspective of Reliable Machine Learning](https://mlsanity.com/paper/2101.01366/) (2021)
- [Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph](https://mlsanity.com/paper/2109.04400/) (2021)
- [Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification](https://mlsanity.com/paper/2309.13543/) (2023)
- [Soft Contextualized Encoder For User Defined Text Classification](https://mlsanity.com/paper/2601.03450/) (2026)

## Abstract (arXiv)

Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.