HFT-ONLSTM：层次化的多标签文本分类
HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification
摘要：
在现实世界中，许多重要的分类问题都是由一个层次结构或分类法中的大量密切相关的类别组成的。层次多标签文本分类（HMTC）具有更高的准确率，在一个层次或分类组织密切相关的类别的大集合已经成为一个具有挑战性的问题。在本文中，我们提出了一种基于有序神经LSTM神经网络（简称HFT-ONLSTM）的分层和微调方法，以实现更准确的逐层HMTC。首先，我们提出了一种新的方法来学习联合嵌入的基础上，父类别标签和文本数据，准确地捕捉类别标签和文本的联合功能。第二，采用微调技术的训练参数，这样的文本分类结果在上层应该有助于在较低的分类。最后，通过在两个基准数据集上与现有的层次化和平面化多标签文本分类方法进行比较，实验结果表明，HFT-ONLSTM方法的性能优于其他方法，特别是在降低计算成本的同时获得了更好的性能.
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
