Abstract
Motivation: Despite recent advancements in semantic representation driven by pre-trained and large-scale language models, addressing long tail challenges in multi-label text classification remains a significant issue. Long tail challenges have persistently posed difficulties in accurately classifying less frequent labels. Current approaches often focus on improving text semantics while neglecting the crucial role of label relationships. Results: This paper introduces LabelCoRank, a novel approach inspired by ranking principles. LabelCoRank leverages label co-occurrence relationships to refine initial label classifications through a dual-stage reranking process. The first stage uses initial classification results to form a preliminary ranking. In the second stage, a label co-occurrence matrix is utilized to rerank the preliminary results, enhancing the accuracy and relevance of the final classifications. By integrating the reranked label representations as additional text features, LabelCoRank effectively mitigates long tail issues in multi-labeltext classification. Experimental evaluations on popular datasets including MAG-CS, PubMed, and AAPD demonstrate the effectiveness and robustness of LabelCoRank.
Abstract (translated)
动机：尽管由预训练和大规模语言模型驱动的语义表示取得了近期进展，但在多标签文本分类中的长尾挑战仍然是一个重大问题。长期存在的难题在于准确分类较少出现的标签上。目前的方法通常侧重于改进文本语义，而忽视了标签间关系的关键作用。 结果：本文提出了一种新颖的方法LabelCoRank，该方法受排名原则启发。LabelCoRank通过利用标签共现关系，在双重重排序过程中优化初始标签分类。第一阶段使用初步的分类结果来形成一个初级排名；在第二阶段中，则使用标签共现矩阵对初步结果进行重新排序，以提高最终分类的准确性和相关性。通过将重排序后的标签表示集成到文本特征中，LabelCoRank能够有效缓解多标签文本分类中的长尾问题。实验评估显示，在包括MAG-CS、PubMed和AAPD在内的流行数据集上，LabelCoRank表现出其有效的性能和鲁棒性。
URL
https://arxiv.org/abs/2503.07968
