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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 41–50 of 445 papers

Title	Date	Tasks	Status	Hype	Score
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition	Jun 28, 2021	named-entity-recognitionNamed Entity Recognition	CodeCode Available	1	5
CreoleVal: Multilingual Multitask Benchmarks for Creoles	Oct 30, 2023	Machine TranslationReading Comprehension	CodeCode Available	1	5
RankDNN: Learning to Rank for Few-shot Learning	Nov 28, 2022	Few-Shot Learningimage-classification	CodeCode Available	1	5
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues	Dec 4, 2020	Dialog Relation ExtractionGeneral Classification	CodeCode Available	1	5
DeepStruct: Pretraining of Language Models for Structure Prediction	May 21, 2022	coreference-resolutionCoreference Resolution	CodeCode Available	1	5
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks	Aug 1, 2021	RelationRelation Classification	CodeCode Available	1	5
Document-Level Relation Extraction with Reconstruction	Dec 21, 2020	Document-level Relation ExtractionRelation	CodeCode Available	1	5
Rationale-Enhanced Language Models are Better Continual Relation Learners	Oct 10, 2023	Continual Relation ExtractionRelation	CodeCode Available	1	5
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification	Jul 12, 2022	Drug DiscoveryRelation Classification	CodeCode Available	1	5
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification	Oct 19, 2020	ClassificationGeneral Classification	CodeCode Available	1	5

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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