A Survey on Data Augmentation for Text Classification

By Markus Bayer, Marc-André Kaufhold, Christian Reuter. ACM Computing Surveys, 2021

478 citations recorded by the source corpus.

Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data to regularizing the objective, to limiting the amount of data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims at providing a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/2f4a0e9ece489f7b35fcf42358fda7d77b4ccf21

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wanxiang Che
2. Bohan Li
3. Evgin Göçeri
4. Yutai Hou

Citations in the corpus

• Medical image data augmentation: techniques, comparisons and interpretations (2023, background): “(ii) to improve the performance of specific operations like polyp segmentation (Dorizza 2021) and text classification (Bayer et al. 2021).”
• Data Augmentation Approaches in Natural Language Processing: A Survey (2021, background): “[4] post a survey on DA for text classification only.”