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A review of semi-supervised learning for text classification.

José Marcio Duarte1, Lilian Berton1

  • 1Science and Technology Department, Federal University of São Paulo, Cesare Mansueto Giulio Lattes Ave, 1201, São José dos Campos, SP 12247-014 Brazil.

Artificial Intelligence Review
|February 6, 2023
PubMed
Summary
This summary is machine-generated.

Semi-supervised learning (SSL) addresses big data challenges in text classification by utilizing both labeled and unlabeled data. This review surveys recent SSL applications in text classification, offering insights for researchers.

Keywords:
Machine learningNatural language processingSemi-supervised learningText classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • The proliferation of daily data presents significant big data challenges, particularly in text mining and classification.
  • Obtaining large labeled datasets for text classification is often costly, time-consuming, and difficult.
  • Semi-supervised learning (SSL), which leverages both labeled and unlabeled data, has emerged as a crucial approach to mitigate these challenges.

Purpose of the Study:

  • To provide an up-to-date review of semi-supervised learning (SSL) techniques applied to text classification.
  • To bridge the gap in recent surveys on SSL for text classification.
  • To offer researchers and practitioners a comprehensive overview and useful information in the field.

Main Methods:

  • A systematic literature search was conducted across major academic databases (IEEE Xplore, ACM Digital Library, Science Direct, Springer).
  • 1794 relevant works published in the last five years were retrieved, from which 157 articles were selected for in-depth review.
  • The selected articles were analyzed based on application domains, datasets, languages, text representations, machine learning algorithms, and SSL taxonomy.

Main Results:

  • The review details the application domains, datasets, languages, text representations, and machine learning algorithms used in SSL for text classification.
  • Analysis includes the percentage of labeled data utilized, evaluation metrics, and performance outcomes across the reviewed studies.
  • The findings are organized according to a recent taxonomy of SSL, providing a structured overview of the field.

Conclusions:

  • The study provides a comprehensive summary and organization of recent advancements in semi-supervised learning for text classification.
  • Identified limitations and future trends offer valuable directions for future research and development in this domain.
  • The review serves as a foundational resource for both academic researchers and industry practitioners working with large-scale text data.