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Relation Classification

Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.

Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 311–320 of 445 papers

Title	Date	Tasks	Status	Hype
Joint Extraction of Entity and Relation with Information Redundancy Elimination	Nov 27, 2020	DecoderLanguage Modeling	—Unverified	0
Jointly Extracting Multiple Triplets with Multilayer Translation Constraints	Jul 17, 2019	Knowledge Base Constructionnamed-entity-recognition	—Unverified	0
Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion	Nov 13, 2016	General ClassificationKnowledge Graph Completion	—Unverified	0
Joint Type Inference on Entities and Relations via Graph Convolutional Networks	Jul 1, 2019	RelationRelation Classification	—Unverified	0
KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification	Feb 25, 2020	General ClassificationMeta-Learning	—Unverified	0
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness	May 2, 2023	Entity LinkingLanguage Modeling	—Unverified	0
KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery	May 16, 2024	named-entity-recognitionNamed Entity Recognition	—Unverified	0
LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network	Aug 1, 2017	ArticlesGeneral Classification	—Unverified	0
LabelPrompt: Effective Prompt-based Learning for Relation Classification	Feb 16, 2023	ClassificationContrastive Learning	—Unverified	0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features	Sep 6, 2024	Feature EngineeringFew-Shot Learning	—Unverified	0

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All datasets TACRED SemEval-2010 Task-8 FewRel AbstRCT - Neoplasm CDCP Discovery DRI Corpus MATRES

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeepEx (zero-shot top-10)	F1	76.4	—	Unverified
2	DeepStruct multi-task	F1	74.9	—	Unverified
3	LUKE 483M	F1	72.7	—	Unverified
4	K-Adapter	F1	72	—	Unverified
5	KnowBERT	F1	71.5	—	Unverified
6	MTB Baldini Soares et al. (2019)	F1	71.5	—	Unverified
7	RoBERTa	F1	71.3	—	Unverified
8	SpanBERT	F1	70.8	—	Unverified
9	ERNIE	F1	68	—	Unverified
10	ERNIE	F1	67.97	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BRCNN	F1	86.3	—	Unverified
2	DRNNs	F1	86.1	—	Unverified
3	depLCNN + NS	F1	85.6	—	Unverified
4	SDP-LSTM	F1	83.7	—	Unverified
5	DepNN	F1	83.6	—	Unverified
6	MVRNN	F1	82.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DeepStruct multi-task w/ finetune	F1 (10-way 1-shot)	97.8	—	Unverified
2	DeepEx (zero-shot top-10)	F1	92.9	—	Unverified
3	DeepStruct multi-task	F1 (10-way 1-shot)	92.2	—	Unverified
4	Deepstruct zero-shot	F1 (10-way 1-shot)	67.6	—	Unverified
5	DeepEx (zero-shot top-1)	F1	48.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResAttArg	Macro F1	70.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResAttArg	Macro F1	42.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT	1:1 Accuracy	20.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ResAttArg	Macro F1	37.72	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SCS-EERE	F1	0.83	—	Unverified