层级标签语义引导的极限多标签文本分类策略

极限多标签文本分类任务具有标签集大、类间关系复杂、数据分布不平衡等特点,是具有挑战性的研究热点。现有模型对标签语义信息利用不足,性能有限。对此,该文提出一种利用层级标签语义信息引导的极限多标签文本分类模型提升策略,在训练和预测过程中给予模型层级标签引导的弱监督语义指导信息,利用这种弱监督信息规约多标签文本分类任务中要对应的多标签语义边界。在标准数据集上的实验结果表明,该文所提策略能够有效提升现有模型性能,尤其在短文本数据集中增效显著,宏精准率最高提升21.23%。

An Extreme Multi-label Text Classification Strategy via Hierarchical Label Semantic Guidance

Extreme multi-label text classification (XMTC) is a challenging for large-scale label sets, complex interclass relationship and unbalanced data distribution. To better employ the label semantic information, this paper proposes an XMTC strategy by using semantic guidance from hierarchical labels, which offers weakly supervised semantic information for models to restrict the boundary of multi-label semantics during training and predicting. The experimental results over benchmark datasets show that the strategy can effectively improve performance of existing models, especially for short-text datasets in which the top macro-P of 21.23% is observed.