Abstract
Many important classification problems in real world consist of a large number of categories. Hierarchical multi-label text classification (HMTC) with higher accuracy over large sets of closely related categories organized in a hierarchical structure or taxonomy has become a challenging problem. In this paper, we present a hierarchical fine-tuning deep learning approach for HMTC. A joint embedding approach of words and parent category are utilized by leveraging the hierarchical relations in the hierarchical structure of categories and the textual data. A fine tuning technique is applied to the Ordered Neural LSTM (ONLSTM) neural network such that the text classification results in the upper levels should contribute to the classification in the lower ones. The extensive experiments were made over two benchmark datasets, and the results show that the method proposed in this paper outperforms the state-of-the-art hierarchical and flat multi-label text classification approaches at significantly lower compu-tational cost while maintaining high interpretability.
Abstract (translated)
URL
https://arxiv.org/abs/2004.02555
