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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 81–90 of 445 papers

Title	Date	Tasks	Status	Hype
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification	Mar 5, 2024	Few-Shot Relation ClassificationRelation	—Unverified	0
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction	Feb 28, 2024	ClassificationNatural Language Inference	CodeCode Available	0
Prompting Implicit Discourse Relation Annotation	Feb 7, 2024	ClassificationImplicit Discourse Relation Classification	—Unverified	0
Noise in Relation Classification Dataset TACRED: Characterization and Reduction	Nov 21, 2023	ClassificationRelation	—Unverified	0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling	Nov 9, 2023	Position regressionRelation	—Unverified	0
Discourse Relations Classification and Cross-Framework Discourse Relation Classification Through the Lens of Cognitive Dimensions: An Empirical Investigation	Nov 1, 2023	ClassificationDiscourse Segmentation	—Unverified	0
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning	Oct 31, 2023	Multi-Task LearningRelation	—Unverified	0
A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methods	Oct 29, 2023	ClassificationFew-Shot Learning	—Unverified	0
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation	Oct 18, 2023	Domain AdaptationFew-Shot Relation Classification	CodeCode Available	0
Rethinking Relation Classification with Graph Meaning Representations	Oct 15, 2023	ClassificationNatural Language Understanding	—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