Assigning a set of labels to a given text is a classification problem with many real-world applications, such as recommender systems. Two separate research streams address this issue. Hierarchical Text Classification (HTC) focuses on datasets with label pools of hundreds of entries, accompanied by a semantic label hierarchy. In contrast, eXtreme Multi-Label Text Classification (XML) considers very large sets of labels with up to millions of entries but without an explicit hierarchy. In XML methods, it is common to construct an artificial hierarchy in order to deal with the large label space before or during the training process. Here, we investigate how state-of-the-art HTC models perform when trained and tested on XML datasets and vice versa using three benchmark datasets from each of the two streams. Our results demonstrate that XML models, with their internally constructed hierarchy, are very effective HTC models. HTC models, on the other hand, are not equipped to handle the sheer label set size of XML datasets and achieve poor transfer results. We further argue that for a fair comparison in HTC and XML, more than one metric like F1 should be used but complemented with P@k and R-Precision.

翻译：为给定文本分配一组标签是一个具有许多实际应用的分类问题，例如推荐系统。两个独立的研究方向致力于解决此问题。层次化文本分类（HTC）专注于标签池包含数百个条目并伴有语义标签层次结构的数据集。相比之下，极端多标签文本分类（XML）考虑的是非常庞大的标签集，条目数量可达数百万，但没有明确的层次结构。在XML方法中，通常会在训练过程之前或期间构建一个人工层次结构以处理巨大的标签空间。本文中，我们研究了最先进的HTC模型在XML数据集上进行训练和测试时的表现，反之亦然，使用了来自这两个方向各三个基准数据集。我们的结果表明，XML模型凭借其内部构建的层次结构，是非常有效的HTC模型。另一方面，HTC模型无法处理XML数据集庞大的标签集规模，迁移效果较差。我们进一步指出，为了在HTC和XML中进行公平比较，不应仅使用F1等单一指标，而应辅以P@k和R-Precision。