Hierarchical Label Text Classification Method with Deep-Level Label-Assisted Classification

Abstract

Hierarchical label text classification is a challenging task in the field of natural language processing, where each document needs to be correctly classified into multiple labels with hierarchical structure. However, in the label set, due to the insufficient semantic information contained in the labels and the small number of documents classified under deep-level labels, the training of deep-level labels is insufficient, leading to a significant imbalance in label training. To address this, a hierarchical label text classification method with deep-level label-assisted classification (DLAC) is proposed. The method proposes a deep-level label-assisted classifier, which effectively utilizes text features and rich features of shallow label nodes corresponding to deep label nodes (i.e., shallow label's rich features) on the basis of enhanced label semantics to enhance the classification performance of deep labels. The comparison experiment results with eleven algorithms on three datasets show that the model can effectively improve the classification performance of deep-level labels and achieve good results.