Perera

Data intensive solutions, such as solutions that include machine learning components, are becoming more and more prevalent. The standard way of developing such solutions is to train machine learning models with manually annotated or labeled data for a given task. This methodology assumes the existence of ample human annotated data. Unfortunately, this is often not the case, due to imbalanced distribution of classes and lack of human annotation resources. This challenge is exasperated when thousands of hierarchical classes are introduced.