  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 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 to provide 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.
