SURVEY ON COMPREHENSIVE TECHNIQUES OF TEXT DATA AUGMENTATION

Abstract

In the world of Data Science and machine learning everything starts with data and how well one can use it to get the desired output. But what can be done when enough data is not present. Obviously, overall result will get impacted because of scarcity of data. Hence, to resolve this problem, data augmentation comes into picture which helps to increase dataset size by augmenting it. Data augmentation is an assortment of techniques that facilitates to automatically generate high quality data with the help of existing data. In the field of Natural Language Processing (NLP) is difficult to augment the text due to huge complexity of language. The process of augmenting text data is more challenging and not as straightforward as one might expect. In this study, the way of doing NLP data augmentation and libraries available for it is explored. Data augmentation techniques, comparison and features of the python libraries which can be used for data augmentation is discussed. It will help researchers and data scientist to decide which library to use for their job.