---
title: "Text Classification Using Label Names Only — Meng 2020"
description: "The paper asks whether a text classifier can be trained without any annotated documents, using only each category's name plus an unlabeled corpus."
authors: ["Yu Meng", "Yunyi Zhang", "Jiaxin Huang", "Chenyan Xiong", "Heng Ji", "Chao Zhang", "Jiawei Han"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.724/"
doi: "10.18653/v1/2020.emnlp-main.724"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/meng-2020-text-classification-label-names-only/"
---
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# Text Classification Using Label Names Only: A Language Model Self-Training Approach

**Authors:** [Yu Meng](https://md.research.elamur.ai/free/en/authors/yu-meng/), Yunyi Zhang, [Jiaxin Huang](https://md.research.elamur.ai/free/en/authors/jiaxin-huang/), [Chenyan Xiong](https://md.research.elamur.ai/free/en/authors/chenyan-xiong/), [Heng Ji](https://md.research.elamur.ai/free/en/authors/heng-ji/), [Chao Zhang](https://md.research.elamur.ai/free/en/authors/chao-zhang/), [Jiawei Han](https://md.research.elamur.ai/free/en/authors/jiawei-han/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.724/) · [PDF](https://aclanthology.org/2020.emnlp-main.724.pdf) · [DOI 10.18653/v1/2020.emnlp-main.724](https://doi.org/10.18653/v1/2020.emnlp-main.724) · [OpenAlex](https://openalex.org/W3106109117)

**Citations:** 196 citations (openalex-snapshot · 2026-10-14) · [78](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary

The paper asks whether a text classifier can be trained without any annotated documents, using only each category's name plus an unlabeled corpus. The authors propose LOTClass, which uses a pre-trained BERT model in two roles: as a source of general language knowledge and as the classifier itself. Training has three stages:

1. BERT's masked language model finds words that can substitute for each label name across many contexts, producing a category vocabulary.
2. A new task, Masked Category Prediction (MCP), teaches the model to infer which category a masked word signals from its surrounding context. Only words judged category-indicative in that context are used as supervision.
3. Self-training on the unlabeled corpus extends this word-level skill to whole documents.

LOTClass was tested on four benchmarks covering both topic and sentiment tasks, with one to three label words per class. It reaches roughly 90% accuracy, clearly beats earlier weakly-supervised methods, and approaches semi-supervised and supervised systems. This makes label names a cheap and underused form of supervision for settings where annotation is scarce.

## Key points

- **Benchmarks:** AG News, DBPedia, IMDB and Amazon. Supervision is at most 3 words per class, and usually just 1. The backbone is BERT-base-uncased with no extra resources such as back-translation systems or Wikipedia.
- **Category vocabulary construction:**
  - For each label-name occurrence, the top-50 MLM predictions count as valid replacements.
  - The 100 most frequent replacements form the class vocabulary.
  - NLTK stopwords and words shared across classes are removed.
- **MCP versus simple matching:** MCP addresses two problems with plain keyword matching. Keyword occurrences do not always indicate the category, and the vocabulary has limited coverage. A "BERT w. simple match" baseline gains nothing from self-training, while LOTClass steadily improves.
- **Comparison with supervised BERT:** On AG News, LOTClass performs close to supervised BERT trained with about 48 labeled documents per class.
- **Limitations and code:** Implicit sentiment remains hard to capture from label names alone. The authors suggest active learning and combination with semi-supervised methods as future directions. Code is released as LOTClass (github.com/yumeng5/LOTClass).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

78 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 74 not yet.

- [Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification](https://md.research.elamur.ai/free/en/papers/hu-2022-knowledgeable-prompt-tuning-incorporating-knowledge/)
- [Document-Level Event Argument Extraction by Conditional Generation](https://md.research.elamur.ai/free/en/papers/li-2021-document-level-event-argument-extraction/)
- [Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision](https://md.research.elamur.ai/free/en/papers/li-2024-discourse-relation-prediction-discourse-parsing/)
- [COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{meng2020text,
  title = {{Text Classification Using Label Names Only: A Language Model Self-Training Approach}},
  author = {Meng, Yu and Zhang, Yunyi and Huang, Jiaxin and Xiong, Chenyan and Ji, Heng and Zhang, Chao and Han, Jiawei},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.724},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.724}
}
```

- [← previous Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://md.research.elamur.ai/free/en/papers/acl-2020-kitaev-tetra-tagging-word-synchronous-parsing/)
- [next → TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP](https://md.research.elamur.ai/free/en/papers/morris-2020-textattack-framework-adversarial-attacks-data/)
- [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/5/)

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