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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 401–410 of 445 papers

Title	Date	Tasks	Status	Hype	Score
Errata: Distant Supervision for Relation Extraction with Matrix Completion	Nov 17, 2014	ClassificationGeneral Classification	—Unverified	0	0
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction	Apr 5, 2018	General ClassificationRelation Classification	—Unverified	0	0
Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections	May 3, 2023	graph constructionOptical Character Recognition	—Unverified	0	0
Evaluating Distributional Models of Semantics for Syntactically Invariant Inference	Apr 1, 2012	Relation Classification	—Unverified	0	0
Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data	Jun 21, 2019	Multi-Task Learningnamed-entity-recognition	—Unverified	0	0
Exploiting Noisy Data in Distant Supervision Relation Classification	Jun 1, 2019	ClassificationGeneral Classification	—Unverified	0	0
Exploiting Timegraphs in Temporal Relation Classification	Oct 1, 2014	ClassificationDocument Summarization	—Unverified	0	0
Exploratory Neural Relation Classification for Domain Knowledge Acquisition	Aug 1, 2018	ClassificationGeneral Classification	—Unverified	0	0
Exploring Vector Spaces for Semantic Relations	Sep 1, 2017	ClusteringGeneral Classification	—Unverified	0	0
Extracting Drug-Drug Interactions with Attention CNNs	Aug 1, 2017	Feature EngineeringGeneral 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