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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 281–290 of 445 papers

Title	Date	Tasks	Status	Hype
Logician and Orator: Learning from the Duality between Language and Knowledge in Open Domain	Oct 1, 2018	Open Information Extractionreinforcement-learning	—Unverified	0
Large-scale Exploration of Neural Relation Classification Architectures	Oct 1, 2018	ClassificationGeneral Classification	CodeCode Available	0
Using active learning to expand training data for implicit discourse relation recognition	Oct 1, 2018	Active LearningRelation	CodeCode Available	0
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction	Sep 26, 2018	NegationRelation	CodeCode Available	0
Crowdsourcing Semantic Label Propagation in Relation Classification	Sep 3, 2018	ClassificationGeneral Classification	CodeCode Available	0
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification	Aug 30, 2018	16kDiscourse Parsing	—Unverified	0
Scientific Relation Extraction with Selectively Incorporated Concept Embeddings	Aug 26, 2018	ClassificationGeneral Classification	—Unverified	0
Word-Level Loss Extensions for Neural Temporal Relation Classification	Aug 7, 2018	ClassificationGeneral Classification	CodeCode Available	0
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation	Aug 5, 2018	Decision MakingRelation Classification	—Unverified	0
Neural Relation Classification with Text Descriptions	Aug 1, 2018	ClassificationGeneral 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