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

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Showing 1151–1200 of 1977 papers

TitleStatusHype
Ontology-Style Relation Annotation: A Case Study—0
Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations—0
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction—0
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations—0
WEXEA: Wikipedia EXhaustive Entity Annotation—0
Recurrent Interaction Network for Jointly Extracting Entities andClassifying Relations—0
Reproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers—0
Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships—0
Distantly-Supervised Neural Relation Extraction with Side Information using BERTCode0
A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss—0
Contextualised Graph Attention for Improved Relation ExtractionCode0
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction—0
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents—0
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation—0
Improving Scholarly Knowledge Representation: Evaluating BERT-based Models for Scientific Relation Classification—0
Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction—0
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products—0
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events—0
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction—0
Efficient long-distance relation extraction with DG-SpanBERT—0
DARE: Data Augmented Relation Extraction with GPT-2—0
Named Entities in Medical Case Reports: Corpus and Experiments—0
HIN: Hierarchical Inference Network for Document-Level Relation Extraction—0
Common-Knowledge Concept Recognition for SEVACode0
Cross-Lingual Adaptation Using Universal Dependencies—0
Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction—0
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach—0
Unique Chinese Linguistic Phenomena—0
Neural Relation Prediction for Simple Question Answering over Knowledge Graph—0
Learning Relation Entailment with Structured and Textual InformationCode0
GREG: A Global Level Relation Extraction with Knowledge Graph Embedding—0
A Neural Architecture for Person Ontology population—0
A logic-based relational learning approach to relation extraction: The OntoILPER system—0
Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge—0
Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs—0
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction—0
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction—0
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language ModelsCode0
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task—0
Effective Attention Modeling for Neural Relation ExtractionCode0
Integrating Deep Learning with Logic Fusion for Information Extraction—0
Enhancing Relation Extraction Using Syntactic Indicators and Sentential ContextsCode0
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision—0
Improving Neural Relation Extraction with Positive and Unlabeled Learning—0
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction—0
Integrating Relation Constraints with Neural Relation ExtractorsCode0
Are Noisy Sentences Useless for Distant Supervised Relation Extraction?—0
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical KnowledgeCode0
Improving Relation Classification by Entity Pair Graph—0
Improving Distant Supervised Relation Extraction by Dynamic Neural Network—0
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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