Abstract
Multi-label classification in document data is the task of correctly assigning multiple class labels to each document. However, there is often a semantic hierarchical structure among the assigned labels, and considering the hierarchical structure can improve the accuracy of label prediction. The Multi-label Box Model (MBM) has been proposed as a multi-label classification model that takes into account the semantic hierarchical structure among labels, and its effectiveness has been demonstrated when class labels of all layers are assigned to training data. However, real-world document data posted on user-contributed websites often lack class labels for all layers of the hierarchy. If such data is used to train MBM, the accuracy of label prediction is reduced. In this study, we propose a framework for learning MBM after complementing labels of missing hierarchies by introducing Bidirectional Encoder Representations from Transformers (BERT). The effectiveness of the proposed method is also demonstrated through evaluation experiments, which compare the accuracy of the conventional method and the proposed method when applied to data with missing labels of some hierarchies.
