LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Reranking
This addresses the issue of accurately classifying less frequent labels in multi-label text classification, which is an incremental improvement over existing methods.
The paper tackles the problem of long tail challenges in multi-label text classification by introducing LabelCoRank, which uses label co-occurrence relationships in a dual-stage reranking process to improve accuracy, as demonstrated on datasets like MAG-CS, PubMed, and AAPD.
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.