Comparison of data augmentation methods for legal document classification

Sorting out the legal documents by their subject matter is an essential and time-consuming task due to large amount of data. Many machine learning-based text categorization methods exist, which can resolve this problem. However, these algorithms not perform well if they do have enough training data for every category. Text augmentation Data a widely used technique in learning applications, especially computer vision. Textual has different characteristics than images, so solutions must be applied when need arises. type textual or itself may reduce number that could certain scenario. This paper focuses on classifying them into specific groups matters.