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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 211–220 of 445 papers

Title	Date	Tasks	Status	Hype	Score
Improved Temporal Relation Classification using Dependency Parses and Selective Crowdsourced Annotations	Dec 1, 2012	Dependency ParsingFeature Engineering	—Unverified	0	0
Improving Event Temporal Relation Classification via Auxiliary Label-Aware Contrastive Learning	Oct 1, 2022	Contrastive LearningData Augmentation	—Unverified	0	0
Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text	Jul 1, 2022	Few-Shot Relation ClassificationRelation	—Unverified	0	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	0
Frowning Frodo, Wincing Leia, and a Seriously Great Friendship: Learning to Classify Emotional Relationships of Fictional Characters	Mar 29, 2019	RelationRelation Classification	—Unverified	0	0
Improving Relation Classification by Entity Pair Graph	Nov 17, 2019	ClassificationGeneral Classification	—Unverified	0	0
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification	Jun 1, 2014	ClassificationGeneral Classification	—Unverified	0	0
Improving Scientific Relation Classification with Task Specific Supersense	Dec 1, 2018	ClassificationRelation	—Unverified	0	0
A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods	Oct 29, 2023	ClassificationFew-Shot Learning	—Unverified	0	0
From Learning-to-Match to Learning-to-Discriminate:Global Prototype Learning for Few-shot Relation Classification	Aug 1, 2021	ClassificationFew-Shot Relation Classification	—Unverified	0	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