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