Global Hierarchical Neural Networks using Hierarchical Softmax

This paper presents a framework in which hierarchical softmax is The paper is structured as follows. In Section 2 previous works used to create a global hierarchical classifier. The approach is applicable on hierarchical classifiers and hierarchical softmax is covered. Our for any classification task where there is a natural hierarchy proposal for the hierarchical softmax is presented in Section 3. Then among classes. We show empirical results on four text classification in Section 4 we describe several datasets and Section 5 discusses the datasets. In all datasets the hierarchical softmax improved on experimental setup. In Section 6 we compare the results of models the regular softmax used in a flat classifier in terms of macro-F1 with a regular softmax and with a hierarchical softmax on these and macro-recall.