Text Augmentation

Text augmentation is a process used to enhance the quality and diversity of text data for various applications, such as natural language processing and machine learning. It involves modifying existing text by techniques like synonym replacement, rephrasing, or adding context to create new, varied examples while preserving the original meaning. This helps improve the performance of models that rely on language data by providing them with a richer set of examples, making them more robust and capable of understanding and generating human-like text. Overall, it boosts the effectiveness of text-based algorithms and applications.