# Semi-supervised Classification for Natural Language Processing

arXiv: https://arxiv.org/abs/1409.7612 · HTML: https://mlsanity.com/paper/1409.7612/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2014.

## Built on by (most cited first, 5 of 5)

- [Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification](https://mlsanity.com/paper/2111.09064/) (2021)
- [NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset](https://mlsanity.com/paper/2304.12847/) (2023)
- [IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection](https://mlsanity.com/paper/2104.01566/) (2021)
- [Keeping in Time: Adding Temporal Context to Sentiment Analysis Models](https://mlsanity.com/paper/2309.13562/) (2023)
- [Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Data](https://mlsanity.com/paper/2401.12295/) (2024)

## Abstract (arXiv)

Semi-supervised classification is an interesting idea where classification models are learned from both labeled and unlabeled data. It has several advantages over supervised classification in natural language processing domain. For instance, supervised classification exploits only labeled data that are expensive, often difficult to get, inadequate in quantity, and require human experts for annotation. On the other hand, unlabeled data are inexpensive and abundant. Despite the fact that many factors limit the wide-spread use of semi-supervised classification, it has become popular since its level of performance is empirically as good as supervised classification. This study explores the possibilities and achievements as well as complexity and limitations of semi-supervised classification for several natural langue processing tasks like parsing, biomedical information processing, text classification, and summarization.