Text Data Augmentation

Natural language processing model performance and generalization are greatly enhanced by text data augmentation. This paper introduces nlpaug, a Python library that provides a wide range of text data augmentation techniques. nlpaug offers functionalities for tasks such as synonym replacement, word insertion, word deletion, character-level modifications, and sentence shuffling. We present an overview of the nlpaug library, including its features and capabilities, and discuss the advantages of using nlpaug for text data augmentation. Through experimental evaluation, we demonstrate the effectiveness of nlpaug in enhancing model performance and its versatility in different NLP tasks. The paper concludes by highlighting the potential applications and future directions of text data augmentation using nlpaug.