Data Augmentation in NLP: Concepts, Classifications, and Research Challenges A Deep Dive into Data Augmentation Strategies for Robust NLP Systems

Data Augmentation in NLP: Concepts, Classifications, and Research Challenges A Deep Dive into Data Augmentation Strategies for Robust NLP Systems

Akshada Zinzurade1, Mohammad Aijaz Ahmed2

1Final Year B.Tech Student, Department of Computer Science and Engineering, MGM’s College of Engineering, Nanded, India

2Sr Assistant Professor, Department of Computer Science and Engineering, MGM’s College of Engineering, Nanded, India

Abstract - Natural Language Processing (NLP) has witnessed significant advancements due to the rise of deep learning techniques. However, most NLP models require large annotated datasets to perform effectively. Creating such datasets is expensive and time-consuming. Data augmentation offers a solution by artificially expanding training data, improving model robustness and generalization. This paper presents a comprehensive overview of data augmentation in NLP, outlining its key concepts, classification strategies, and real-world applications. Further, it highlights ongoing research challenges and provides insights into future directions for making augmentation more adaptive and context-aware in language-based systems.

Index Terms — Natural Language Processing, Data Augmentation, Text Generation, NLP Pipelines, Deep Learning