# Predicting the Effectiveness of Self-Training: Application to Sentiment Classification

arXiv: https://arxiv.org/abs/1601.03288 · HTML: https://mlsanity.com/paper/1601.03288/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2016.

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

- [Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP](https://mlsanity.com/paper/2110.03618/) (2021)
- [Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling](https://mlsanity.com/paper/2101.04758/) (2021)
- [Self-training Strategies for Sentiment Analysis: An Empirical Study](https://mlsanity.com/paper/2309.08777/) (2023)
- [Zero-Resource Multi-Dialectal Arabic Natural Language Understanding](https://mlsanity.com/paper/2104.06591/) (2021)
- [Investigating Semi-Supervised Learning Algorithms in Text Datasets](https://mlsanity.com/paper/2401.01843/) (2024)

## Abstract (arXiv)

The goal of this paper is to investigate the connection between the performance gain that can be obtained by selftraining and the similarity between the corpora used in this approach. Self-training is a semi-supervised technique designed to increase the performance of machine learning algorithms by automatically classifying instances of a task and adding these as additional training material to the same classifier. In the context of language processing tasks, this training material is mostly an (annotated) corpus. Unfortunately self-training does not always lead to a performance increase and whether it will is largely unpredictable. We show that the similarity between corpora can be used to identify those setups for which self-training can be beneficial. We consider this research as a step in the process of developing a classifier that is able to adapt itself to each new test corpus that it is presented with.