Many important classification problems in the real-world consist of a large
number of closely related categories in a hierarchical structure or taxonomy.
Hierarchical multi-label text classification (HMTC) with higher accuracy over
large sets of closely related categories organized in a hierarchy or taxonomy
has become a challenging problem. In this paper, we present a hierarchical and
fine-tuning approach based on the Ordered Neural LSTM neural network,
abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC. First, we
present a novel approach to learning the joint embeddings based on parent
category labels and textual data for accurately capturing the joint features of
both category labels and texts. Second, a fine tuning technique is adopted for
training parameters such that the text classification results in the upper
level should contribute to the classification in the lower one. At last, the
comprehensive analysis is made based on extensive experiments in comparison
with the state-of-the-art hierarchical and flat multi-label text classification
approaches over two benchmark datasets, and the experimental results show that
our HFT-ONLSTM approach outperforms these approaches, in particular reducing
computational costs while achieving superior performance.

本文提出了一个基于有序神经 LSTM 网络的分层和微调方法，缩写为 HFT-ONLSTM，以更准确地进行逐层 HMTC。 联合嵌入的新方法，基于父类别标签和文本数据进行准确捕捉文本和类别标签的联合特征，采用微调技术来训练参数，以使得上层文本分类结果对下层分类贡献。 实验结果表明，我们的方法在减少计算成本的同时，实现了优越的性能，优于基于分层和平面多标签文本分类方法的最新技术水平。