  In this paper, we investigate the effectiveness of integrating a hierarchical
taxonomy of labels as prior knowledge into the learning algorithm of a flat
classifier. We introduce two methods to integrate the hierarchical taxonomy as
an explicit regularizer into the loss function of learning algorithms. By
reasoning on a hierarchical taxonomy, a neural network alleviates its output
distributions over the classes, allowing conditioning on upper concepts for a
minority class. We limit ourselves to the flat classification task and provide
our experimental results on two industrial in-house datasets and two public
benchmarks, RCV1 and Amazon product reviews. Our obtained results show the
significant effect of a taxonomy in increasing the performance of a learner in
semisupervised multi-class classification and the considerable results obtained
in a fully supervised fashion.
