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Multi-Label Text Classification

According to Wikipedia "In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."

Papers

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Showing 1–10 of 171 papers

TitleStatusHype
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification—0
QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification—0
Task-Informed Anti-Curriculum by Masking Improves Downstream Performance on TextCode0
Retrieval-augmented Encoders for Extreme Multi-label Text Classification—0
Hierarchical Text Classification (HTC) vs. eXtreme Multilabel Classification (XML): Two Sides of the Same MedalCode0
A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data—0
Large Language Models for Patient Comments Multi-Label Classification—0
Don't Just Pay Attention, PLANT It: Transfer L2R Models to Fine-tune Attention in Extreme Multi-Label Text Classification—0
A Novel Method to Metigate Demographic and Expert Bias in ICD Coding with Causal Inference—0
Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive LearningCode0
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All datasetsCC3M-TagMaskReuters-21578AAPDFreecodeBVICTOREUR-LexMVICTOR (theme)SVICTOR (theme)MIMIC-IIIMIMIC-III-50Amazon-12KDataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1XGBoostAverage F10.88—Unverified
2SVMAverage F10.78—Unverified
3NBAverage F10.63—Unverified