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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
Temporal Event Knowledge Acquisition via Identifying Narratives	May 28, 2018	General ClassificationRelation Classification	—Unverified	0
Syntactic Dependency Representations in Neural Relation Classification	May 28, 2018	ClassificationGeneral Classification	—Unverified	0
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction	May 24, 2018	RelationRelation Classification	—Unverified	0
Classifying medical relations in clinical text via convolutional neural networks	May 17, 2018	ClassificationGeneral Classification	—Unverified	0
Composite Semantic Relation Classification	May 16, 2018	BIG-bench Machine LearningClassification	—Unverified	0
Chinese Relation Classification using Long Short Term Memory Networks	May 1, 2018	ClassificationGeneral Classification	—Unverified	0
Systems' Agreements and Disagreements in Temporal Processing: An Extensive Error Analysis of the TempEval-3 Task	May 1, 2018	Natural Language InferenceQuestion Answering	—Unverified	0
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers	Apr 24, 2018	ClassificationGeneral Classification	—Unverified	0
LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification	Apr 19, 2018	ClassificationGeneral Classification	—Unverified	0
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph	Apr 16, 2018	General ClassificationImplicit Discourse Relation Classification	—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