论文标题
利用全球和本地层次结构进行分层文本分类
Exploiting Global and Local Hierarchies for Hierarchical Text Classification
论文作者
论文摘要
分层文本分类旨在利用多标签文本分类中的标签层次结构。现有方法在全局视图中编码标签层次结构，其中标签层次结构被视为包含所有标签的静态层次结构。由于全局层次结构与文本样本无关，因此使这些方法难以利用层次结构信息。与全局层次结构相反，本地层次结构是与每个文本样本相对应的结构化标签层次结构。它是动态的，并且与文本样本有关，这在先前的方法中被忽略。为了利用全球和本地层次结构，我们建议使用全球和本地层次结构（HBGL）提出层次结构指导的BERT（HBGL），它利用了大规模参数以及BERT的先前语言知识来模拟全球和局部层次结构。更多地，HBGL避免了与语义和层次模型的有意融合，以避免使用语义和层次模型的模型和HIRERARCT模型，并构建模型的模型和HIREARCH模型。最先进的方法HGCLR，我们的方法在三个基准数据集上取得了重大改进。
Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels. Since global hierarchy is static and irrelevant to text samples, it makes these methods hard to exploit hierarchical information. Contrary to global hierarchy, local hierarchy as a structured labels hierarchy corresponding to each text sample. It is dynamic and relevant to text samples, which is ignored in previous methods. To exploit global and local hierarchies,we propose Hierarchy-guided BERT with Global and Local hierarchies (HBGL), which utilizes the large-scale parameters and prior language knowledge of BERT to model both global and local hierarchies.Moreover,HBGL avoids the intentional fusion of semantic and hierarchical modules by directly modeling semantic and hierarchical information with BERT.Compared with the state-of-the-art method HGCLR,our method achieves significant improvement on three benchmark datasets.
