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 41–50 of 171 papers

Title	Date	Tasks	Status
A Multi-Dimensional, Cross-Domain and Hierarchy-Aware Neural Architecture for ISO-Standard Dialogue Act Tagging	Oct 1, 2022	Multi Label Text ClassificationMulti-Label Text Classification	—Unverified
BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification	Oct 27, 2022	Multi Label Text ClassificationMulti-Label Text Classification	—Unverified
A Debiased Nearest Neighbors Framework for Multi-Label Text Classification	Aug 6, 2024	Contrastive LearningMulti Label Text Classification	—Unverified
BdLAN:BERTdoc Label Attention Networks for Multi-label text classification	May 16, 2021	ClassificationInformation Retrieval	—Unverified
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN	Jul 1, 2022	Deep LearningMulti Label Text Classification	—Unverified
Active learning for medical code assignment	Apr 12, 2021	Active LearningDecision Making	—Unverified
Extreme Multi-label Text Classification with Pseudo Label Descriptions	Nov 16, 2021	ClassificationMulti Label Text Classification	—Unverified
Extreme Zero-Shot Learning for Extreme Text Classification	Jan 16, 2022	ClassificationMulti Label Text Classification	—Unverified
Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification	May 24, 2022	Multi Label Text ClassificationMulti-Label Text Classification	—Unverified
Enhancing Chinese Multi-Label Text Classification Performance with Response-based Knowledge Distillation	Nov 1, 2022	Knowledge DistillationMulti Label Text Classification	—Unverified

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All datasets CC3M-TagMask Reuters-21578 AAPD Freecode BVICTOR EUR-Lex MVICTOR (theme)SVICTOR (theme)MIMIC-III MIMIC-III-50 Amazon-12K Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TTD (w/ fine-tuning)	Precision	88.3	—	Unverified
2	TTD (w/o fine-tuning)	Precision	82.9	—	Unverified
3	Qwen-72B	Precision	69.3	—	Unverified
4	NLTK	Precision	59.8	—	Unverified
5	Vicuna-33B	Precision	52.7	—	Unverified
6	Vicuna-7B	Precision	44.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CB-NTR	Micro-F1	90.74	—	Unverified
2	NTR-FL	Micro-F1	90.7	—	Unverified
3	DB	Micro-F1	90.62	—	Unverified
4	MAGNET	Micro-F1	89.9	—	Unverified
5	CNLE	Micro-F1	89.9	—	Unverified
6	VLAWE	Micro-F1	89.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSAN	P@1	85.28	—	Unverified
2	LAHA	P@1	84.48	—	Unverified
3	LW-PT	Micro F1	72.8	—	Unverified
4	CNLE	Micro F1	71.7	—	Unverified
5	MAGNET	F1	69.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TagBERT	F1-score	46	—	Unverified
2	TagCNN	F1-score	45.3	—	Unverified
3	TagMulRec	F1-score	36.4	—	Unverified
4	EnTagRec	F1-score	36	—	Unverified
5	FastTagRec	F1-score	33.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XGBoost	Average F1	0.88	—	Unverified
2	SVM	Average F1	0.78	—	Unverified
3	NB	Average F1	0.63	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	bert-base	P@5	68.7	—	Unverified
2	NLP-Cap	P@5	52.83	—	Unverified
3	LAHA	P@5	50.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XGBoost	Average F1	0.89	—	Unverified
2	SVM	Average F1	0.66	—	Unverified
3	NB	Average F1	0.38	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XGBoost	Average F1	0.89	—	Unverified
2	SVM	Average F1	0.82	—	Unverified
3	NB	Average F1	0.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HLAN	AUC	0.92	—	Unverified
2	Feed-forward NN	Precision	0.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	D2SBERT using Sequence Attention	Micro-F1	68.56	—	Unverified
2	HLAN	Micro-F1	64.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LAHA	P@1	94.87	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Bert	1:1 Accuracy	0.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LAHA	P@1	54.38	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DECAF	Precision@1	38.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ECLARE	Precision@1	40.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HiddeN	Macro-F1	47.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MAGNET	Micro-F1	88.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MAGNET	Micro-F1	56.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT	F1	66.83	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LAHA	P@1	84.18	—	Unverified