SOTAVerified

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

Showing 1–25 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
Exploring space efficiency in a tree-based linear model for extreme multi-label classification—0
A Debiased Nearest Neighbors Framework for Multi-Label Text Classification—0
A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts—0
Open-world Multi-label Text Classification with Extremely Weak SupervisionCode1
LegalTurk Optimized BERT for Multi-Label Text Classification and NER—0
ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks—0
Learning label-label correlations in Extreme Multi-label Classification via Label Features—0
Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling—0
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag BiasCode1
KeNet:Knowledge-enhanced Doc-Label Attention Network for Multi-label text classification—0
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification—0
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification—0
Compositional Generalization for Multi-label Text Classification: A Data-Augmentation ApproachCode1
Well-calibrated Confidence Measures for Multi-label Text Classification with a Large Number of Labels—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TTD (w/ fine-tuning)Precision88.3—Unverified
2TTD (w/o fine-tuning)Precision82.9—Unverified
3Qwen-72BPrecision69.3—Unverified
4NLTKPrecision59.8—Unverified
5Vicuna-33BPrecision52.7—Unverified
6Vicuna-7BPrecision44.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CB-NTRMicro-F190.74—Unverified
2NTR-FLMicro-F190.7—Unverified
3DBMicro-F190.62—Unverified
4MAGNETMicro-F189.9—Unverified
5CNLEMicro-F189.9—Unverified
6VLAWEMicro-F189.3—Unverified
#ModelMetricClaimedVerifiedStatus
1LSANP@185.28—Unverified
2LAHAP@184.48—Unverified
3LW-PTMicro F172.8—Unverified
4CNLEMicro F171.7—Unverified
5MAGNETF169.6—Unverified
#ModelMetricClaimedVerifiedStatus
1TagBERTF1-score46—Unverified
2TagCNNF1-score45.3—Unverified
3TagMulRecF1-score36.4—Unverified
4EnTagRecF1-score36—Unverified
5FastTagRecF1-score33.2—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoostAverage F10.88—Unverified
2SVMAverage F10.78—Unverified
3NBAverage F10.63—Unverified
#ModelMetricClaimedVerifiedStatus
1bert-baseP@568.7—Unverified
2NLP-CapP@552.83—Unverified
3LAHAP@550.71—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoostAverage F10.89—Unverified
2SVMAverage F10.66—Unverified
3NBAverage F10.38—Unverified
#ModelMetricClaimedVerifiedStatus
1XGBoostAverage F10.89—Unverified
2SVMAverage F10.82—Unverified
3NBAverage F10.51—Unverified
#ModelMetricClaimedVerifiedStatus
1HLANAUC0.92—Unverified
2Feed-forward NNPrecision0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1D2SBERT using Sequence AttentionMicro-F168.56—Unverified
2HLANMicro-F164.1—Unverified
#ModelMetricClaimedVerifiedStatus
1LAHAP@194.87—Unverified
#ModelMetricClaimedVerifiedStatus
1Bert1:1 Accuracy0.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LAHAP@154.38—Unverified
#ModelMetricClaimedVerifiedStatus
1DECAFPrecision@138.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ECLAREPrecision@140.74—Unverified
#ModelMetricClaimedVerifiedStatus
1HiddeNMacro-F147.3—Unverified
#ModelMetricClaimedVerifiedStatus
1MAGNETMicro-F188.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MAGNETMicro-F156.8—Unverified
#ModelMetricClaimedVerifiedStatus
1BERTF166.83—Unverified
#ModelMetricClaimedVerifiedStatus
1LAHAP@184.18—Unverified