SOTAVerified

Relation Extraction

Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.

Source: Deep Residual Learning for Weakly-Supervised Relation Extraction

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1050 of 1977 papers

TitleStatusHype
Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics—0
The Dots Have Their Values: Exploiting the Node-Edge Connections in Graph-based Neural Models for Document-level Relation Extraction—0
mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab ProtocolsCode0
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information—0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction—0
Investigation of BERT Model on Biomedical Relation Extraction Based on Revised Fine-tuning Mechanism—0
A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification—0
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation ExtractionCode1
RuREBus: a Case Study of Joint Named Entity Recognition and Relation Extraction from e-Government Domain—0
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated PromptsCode1
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab ProtocolsCode1
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair LinkingCode1
A Frustratingly Easy Approach for Entity and Relation ExtractionCode1
Effective Distant Supervision for Temporal Relation ExtractionCode1
Exploit Multiple Reference Graphs for Semi-supervised Relation Extraction—0
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context PoolingCode1
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From CharactersCode1
Bootleg: Chasing the Tail with Self-Supervised Named Entity DisambiguationCode1
Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction—0
Cross-Lingual Relation Extraction with Transformers—0
TDRE: A Tensor Decomposition Based Approach for Relation Extraction—0
Named Entity Recognition and Relation Extraction using Enhanced Table Filling by Contextualized RepresentationsCode1
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion—0
Joint Constrained Learning for Event-Event Relation Extraction—0
BioMegatron: Larger Biomedical Domain Language Model—0
Relation Classification as Two-way Span-Prediction—0
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence EncodersCode1
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented AttentionCode0
Exposing Shallow Heuristics of Relation Extraction Models with Challenge DataCode1
Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations—0
Semi-supervised Relation Extraction via Incremental Meta Self-TrainingCode1
Learning from Context or Names? An Empirical Study on Neural Relation ExtractionCode1
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentionCode1
基于深度学习的实体关系抽取研究综述(Review of Entity Relation Extraction based on deep learning)—0
Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction—0
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-trainingCode0
RDSGAN: Rank-based Distant Supervision Relation Extraction with Generative Adversarial Framework—0
Double Graph Based Reasoning for Document-level Relation ExtractionCode1
DWIE: an entity-centric dataset for multi-task document-level information extractionCode1
Reinforcement Learning-based N-ary Cross-Sentence Relation Extraction—0
Clustering-based Unsupervised Generative Relation Extraction—0
A Comparative Study on Structural and Semantic Properties of Sentence EmbeddingsCode0
Global-to-Local Neural Networks for Document-Level Relation ExtractionCode1
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!Code1
Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text—0
Relation Extraction from Biomedical and Clinical Text: Unified Multitask Learning Framework—0
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural NetworkCode1
Finding Influential Instances for Distantly Supervised Relation Extraction—0
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text—0
Show:102550
← PrevPage 21 of 40Next →
All datasetsDocREDTACREDSemEval-2010 Task-8ACE 2005CoNLL04Adverse Drug Events (ADE) CorpusWebNLGChemProtACE 2004NYT11-HRLCDRGDA

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DREEAMF167.53—Unverified
2KD-Rb-lF167.28—Unverified
3SSAN-RoBERTa-large+AdaptationF165.92—Unverified
4SAIS-RoBERTa-largeF165.11—Unverified
5Eider-RoBERTa-largeF164.79—Unverified
6DocuNet-RoBERTa-largeF164.55—Unverified
7CGM2IR-RoBERTalargeF163.89—Unverified
8SETE-Roberta-largeF163.74—Unverified
9ATLOP-RoBERTa-largeF163.4—Unverified
10DRE-MIR-BERTbaseF163.15—Unverified
#ModelMetricClaimedVerifiedStatus
1RAG4REF186.6—Unverified
2DeepStruct multi-task w/ finetuneF176.8—Unverified
3UNiST (LARGE)F175.5—Unverified
4RE-MCF175.4—Unverified
5GenPT (T5)F175.3—Unverified
6RECENT+SpanBERTF175.2—Unverified
7SuRE (PEGASUS-large)F175.1—Unverified
8EXOBRAINF175—Unverified
9Relation ReductionF174.8—Unverified
10RoBERTa-large-typed-markerF174.6—Unverified
#ModelMetricClaimedVerifiedStatus
1SPF191.9—Unverified
2RIFREF191.3—Unverified
3REDNF191—Unverified
4SPOTF190.6—Unverified
5KLGF190.5—Unverified
6RELAF190.4—Unverified
7Skeleton-Aware BERTF190.36—Unverified
8KnowPromptF190.3—Unverified
9LUKEF190.3—Unverified
10EPGNNF190.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Span-levelNER Micro F185.98—Unverified
2Dual Pointer Network(multi-head)Relation classification F180.8—Unverified
3Dual Pointer NetworkRelation classification F180.5—Unverified
4PL-MarkerRE Micro F173—Unverified
5ASP+T5-3BRE Micro F172.7—Unverified
6GoLLIERE Micro F170.1—Unverified
7Ours: cross-sentence ALBRE Micro F169.4—Unverified
8MGERE+ Micro F168.2—Unverified
9HySPA (ours) w/ RoBERTaRelation F168.2—Unverified
10RNN+CNNRelation classification F167.7—Unverified
#ModelMetricClaimedVerifiedStatus
1ReLiK-LargeRE+ Micro F178.1—Unverified
2REBELRE+ Macro F1 76.65—Unverified
3ASP+T0-3BRE+ Micro F176.3—Unverified
4Table-SequenceRE+ Macro F1 75.4—Unverified
5SpERTRE+ Macro F1 72.87—Unverified
6DeeperRE+ Macro F1 72.63—Unverified
7TANLRE+ Micro F172.6—Unverified
8TablERTRE+ Micro F172.6—Unverified
9TriMFRE+ Micro F172.35—Unverified
10Multi-turn QARE+ Micro F168.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PFN (ALBERT XXL, average aggregation)RE+ Macro F183.9—Unverified
2DeeperRE+ Macro F183.74—Unverified
3PFN (ALBERT XXL, no aggregation)RE+ Macro F183.2—Unverified
4SpERT.PL (without overlap and BioBERT)RE+ Macro F182.39—Unverified
5REBEL (including overlapping entities)RE+ Macro F182.2—Unverified
6SpERT.PL (with overlap and BioBERT)RE+ Macro F182.03—Unverified
7CMANRE+ Macro F181.14—Unverified
8Table-SequenceRE+ Macro F180.1—Unverified
9CLDR + CLNERRE+ Macro F179.97—Unverified
10SpERT (without overlap)RE+ Macro F179.24—Unverified
#ModelMetricClaimedVerifiedStatus
1UniRelF194.7—Unverified
2PFNF193.6—Unverified
3SPNF193.4—Unverified
4TDEERF193.1—Unverified
5RIFREF192.6—Unverified
6TPLinkerF191.9—Unverified
7HBT (CasRel)F191.8—Unverified
8RIN (BERT, K=2)F190.1—Unverified
9CGT(UniLM)F183.4—Unverified