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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 331–340 of 445 papers

Title	Date	Tasks	Status	Hype
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging	Nov 1, 2017	Relation ClassificationWord Embeddings	—Unverified	0
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words	Sep 19, 2017	General ClassificationRelation Classification	CodeCode Available	0
Exploring Vector Spaces for Semantic Relations	Sep 1, 2017	ClusteringGeneral Classification	—Unverified	0
Argument Relation Classification Using a Joint Inference Model	Sep 1, 2017	Argument MiningClassification	—Unverified	0
Inter-Weighted Alignment Network for Sentence Pair Modeling	Sep 1, 2017	Machine TranslationNatural Language Inference	—Unverified	0
Recognizing Reputation Defence Strategies in Critical Political Exchanges	Sep 1, 2017	General ClassificationRelation	—Unverified	0
What matters in a transferable neural network model for relation classification in the biomedical domain?	Aug 11, 2017	BIG-bench Machine LearningGeneral Classification	—Unverified	0
Extracting Drug-Drug Interactions with Attention CNNs	Aug 1, 2017	Feature EngineeringGeneral Classification	—Unverified	0
A non-DNN Feature Engineering Approach to Dependency Parsing -- FBAML at CoNLL 2017 Shared Task	Aug 1, 2017	Dependency ParsingFeature Engineering	—Unverified	0
Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention	Aug 1, 2017	Feature EngineeringGeneral 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