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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 121–130 of 445 papers

Title	Date	Tasks	Status	Hype
Direct Fact Retrieval from Knowledge Graphs without Entity Linking	May 21, 2023	Entity DisambiguationEntity Linking	—Unverified	0
Bidirectional Recurrent Convolutional Neural Network for Relation Classification	Aug 1, 2016	ClassificationGeneral Classification	—Unverified	0
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction	May 24, 2018	RelationRelation Classification	—Unverified	0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora	Jun 1, 2014	Active LearningClassification	—Unverified	0
Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks	Sep 1, 2019	Binary ClassificationClassification	—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
CogALex-V Shared Task: CGSRC - Classifying Semantic Relations using Convolutional Neural Networks	Dec 1, 2016	Machine TranslationParaphrase Generation	—Unverified	0
Aspectual Type and Temporal Relation Classification	Apr 1, 2012	ClassificationDocument Summarization	—Unverified	0
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification	Jan 10, 2021	Few-Shot Relation ClassificationRelation	—Unverified	0
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification	Aug 1, 2018	General ClassificationImplicit Discourse Relation 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