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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 361–370 of 445 papers

Title	Date	Tasks	Status	Hype
On the contribution of word embeddings to temporal relation classification	Dec 1, 2016	ClassificationGeneral Classification	—Unverified	0
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction	Dec 1, 2016	ClassificationEntity Extraction using GAN	CodeCode Available	0
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks	Dec 1, 2016	Multi-Label LearningRelation	—Unverified	0
Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion	Nov 13, 2016	General ClassificationKnowledge Graph Completion	—Unverified	0
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification	Nov 1, 2016	Feature EngineeringGeneral Classification	—Unverified	0
A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification	Nov 1, 2016	General ClassificationInformation Retrieval	—Unverified	0
CogALex-V Shared Task: LexNET - Integrated Path-based and Distributional Method for the Identification of Semantic Relations	Oct 27, 2016	ClassificationGeneral Classification	CodeCode Available	0
Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison	Sep 3, 2016	Dictionary LearningImage Retrieval	—Unverified	0
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features	Aug 27, 2016	Dependency ParsingGeneral Classification	—Unverified	0
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification	Aug 1, 2016	General ClassificationQuestion Answering	CodeCode Available	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