# LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Reranking

arXiv: https://arxiv.org/abs/2503.07968 · HTML: https://mlsanity.com/paper/2503.07968/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [Star-Transformer](https://mlsanity.com/paper/1902.09113/) (2019)
- [Dynamic Curriculum Learning for Imbalanced Data Classification](https://mlsanity.com/paper/1901.06783/) (2019)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [Learning Fast Sample Re-weighting Without Reward Data](https://mlsanity.com/paper/2109.03216/) (2021)
- [Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations](https://mlsanity.com/paper/2307.05722/) (2023)
- [Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution](https://mlsanity.com/paper/2109.04712/) (2021)
- [MATCH: Metadata-Aware Text Classification in A Large Hierarchy](https://mlsanity.com/paper/2102.07349/) (2021)
- [Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning](https://mlsanity.com/paper/2106.03103/) (2021)
- [LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection](https://mlsanity.com/paper/2401.04749/) (2024)
- [Multi-Label Text Classification using Attention-based Graph Neural Network](https://mlsanity.com/paper/2003.11644/) (2020)
- [Does Head Label Help for Long-Tailed Multi-Label Text Classification](https://mlsanity.com/paper/2101.09704/) (2021)
- [LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification](https://mlsanity.com/paper/2103.14620/) (2021)
- [KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling](https://mlsanity.com/paper/2203.06835/) (2022)
- [SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter](https://mlsanity.com/paper/2312.11553/) (2023)
- [GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification](https://mlsanity.com/paper/2201.11582/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining](https://mlsanity.com/paper/2602.15330/) (2026)

## Abstract (arXiv)

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.