# Multi-label Dataless Text Classification with Topic Modeling

arXiv: https://arxiv.org/abs/1711.01563 · HTML: https://mlsanity.com/paper/1711.01563/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2017.

## Built on by (most cited first, 14 of 14)

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- [Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach](https://mlsanity.com/paper/2402.05296/) (2024)
- [Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals](https://mlsanity.com/paper/2112.03009/) (2021)
- [Research on Intellectual Property Resource Profile and Evolution Law](https://mlsanity.com/paper/2204.06221/) (2022)
- [Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language](https://mlsanity.com/paper/2210.10903/) (2022)
- [Using Language Models on Low-end Hardware](https://mlsanity.com/paper/2305.02350/) (2023)
- [Exploring Description-Augmented Dataless Intent Classification](https://mlsanity.com/paper/2407.17862/) (2024)
- [Fine-Tuning Topics through Weighting Aspect Keywords](https://mlsanity.com/paper/2502.08496/) (2025)
- [Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models](https://mlsanity.com/paper/2511.04248/) (2025)
- [Knowledge Graph-Enhanced Zero-Shot Topic Classification: A Multi-Strategy Comparative Study](https://mlsanity.com/paper/2605.30465/) (2026)

## Abstract (arXiv)

Manually labeling documents is tedious and expensive, but it is essential for training a traditional text classifier. In recent years, a few dataless text classification techniques have been proposed to address this problem. However, existing works mainly center on single-label classification problems, that is, each document is restricted to belonging to a single category. In this paper, we propose a novel Seed-guided Multi-label Topic Model, named SMTM. With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document. In SMTM, each category is associated with a single category-topic which covers the meaning of the category. To accommodate with multi-labeled documents, we explicitly model the category sparsity in SMTM by using spike and slab prior and weak smoothing prior. That is, without using any threshold tuning, SMTM automatically selects the relevant categories for each document. To incorporate the supervision of the seed words, we propose a seed-guided biased GPU (i.e., generalized Polya urn) sampling procedure to guide the topic inference of SMTM. Experiments on two public datasets show that SMTM achieves better classification accuracy than state-of-the-art alternatives and even outperforms supervised solutions in some scenarios.