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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 41–50 of 171 papers

Title	Date	Tasks	Status	Score
Label-Wise Document Pre-Training for Multi-Label Text Classification	Aug 15, 2020	ClassificationDocument Classification	CodeCode Available	5
Label-Specific Document Representation for Multi-Label Text Classification	Nov 1, 2019	ClassificationGeneral Classification	CodeCode Available	5
LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network	Sep 23, 2020	Multi Label Text ClassificationMulti-Label Text Classification	CodeCode Available	5
I-AID: Identifying Actionable Information from Disaster-related Tweets	Aug 4, 2020	Graph AttentionManagement	CodeCode Available	5
Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Models	Dec 12, 2022	Multi Label Text ClassificationMulti-Label Text Classification	CodeCode Available	5
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning	Apr 4, 2021	BenchmarkingMulti Label Text Classification	CodeCode Available	5
Investigating Capsule Networks with Dynamic Routing for Text Classification	Mar 29, 2018	ClassificationGeneral Classification	CodeCode Available	5
Hierarchical Text Classification (HTC) vs. eXtreme Multilabel Classification (XML): Two Sides of the Same Medal	Nov 20, 2024	ClassificationMulti Label Text Classification	CodeCode Available	5
PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model	May 14, 2019	ClassificationGeneral Classification	CodeCode Available	5
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi	Jan 20, 2021	Multi-class ClassificationMulti-Label Text Classification	CodeCode Available	5

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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