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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 371–380 of 445 papers

Title	Date	Tasks	Status	Hype
On the Compositionality and Semantic Interpretation of English Noun Compounds	Aug 1, 2016	Relation ClassificationRepresentation Learning	CodeCode Available	0
Discourse Relation Sense Classification Systems for CoNLL-2016 Shared Task	Aug 1, 2016	ClassificationGeneral Classification	—Unverified	0
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification	Aug 1, 2016	General ClassificationQuestion Answering	CodeCode Available	0
Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM	Aug 1, 2016	Relation Classification	—Unverified	0
Context-aware Argumentative Relation Mining	Aug 1, 2016	Argument MiningDocument Summarization	—Unverified	0
Bidirectional Recurrent Convolutional Neural Network for Relation Classification	Aug 1, 2016	ClassificationGeneral Classification	—Unverified	0
Learning Semantically and Additively Compositional Distributional Representations	Jun 8, 2016	General ClassificationRelation Classification	CodeCode Available	0
Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features	Jun 7, 2016	Discourse ParsingGeneral Classification	—Unverified	0
VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval	Jun 1, 2016	Domain AdaptationNatural Language Inference	—Unverified	0
Dependency Based Embeddings for Sentence Classification Tasks	Jun 1, 2016	ChunkingClassification	—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