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 151–200 of 1977 papers

TitleStatusHype
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity LinkingCode1
EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation ExtractionCode1
GraphER: A Structure-aware Text-to-Graph Model for Entity and Relation ExtractionCode2
Variational Multi-Modal Hypergraph Attention Network for Multi-Modal Relation Extraction—0
A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents—0
Leveraging Data Augmentation for Process Information ExtractionCode0
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models—0
Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework—0
Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locations—0
A Two Dimensional Feature Engineering Method for Relation ExtractionCode0
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation—0
Evaluating Generative Language Models in Information Extraction as Subjective Question CorrectionCode0
READ: Improving Relation Extraction from an ADversarial PerspectiveCode0
EGTR: Extracting Graph from Transformer for Scene Graph GenerationCode2
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction TasksCode1
Guided Distant Supervision for Multilingual Relation Extraction Data: Adapting to a New Language—0
MixRED: A Mix-lingual Relation Extraction DatasetCode0
General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing—0
CHisIEC: An Information Extraction Corpus for Ancient Chinese HistoryCode1
Event Temporal Relation Extraction based on Retrieval-Augmented on LLMs—0
CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction—0
AutoRE: Document-Level Relation Extraction with Large Language ModelsCode2
GraphERE: Jointly Multiple Event-Event Relation Extraction via Graph-Enhanced Event Embeddings—0
Pipelined Biomedical Event Extraction Rivaling Joint Learning—0
Information Extraction: An application to the domain of hyper-local financial data on developing countries—0
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models—0
MRL Parsing Without Tears: The Case of Hebrew—0
Extracting Protein-Protein Interactions (PPIs) from Biomedical Literature using Attention-based Relational Context InformationCode0
Advancing Chinese biomedical text mining with community challenges—0
DP-CRE: Continual Relation Extraction via Decoupled Contrastive Learning and Memory Structure Preservation—0
CODE-ACCORD: A Corpus of Building Regulatory Data for Rule Generation towards Automatic Compliance CheckingCode1
FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation ExtractionCode0
AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models—0
Few-Shot Relation Extraction with Hybrid Visual Evidence—0
Extracting Polymer Nanocomposite Samples from Full-Length DocumentsCode0
Pointing out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials—0
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software EcosystemCode0
Making Pre-trained Language Models Better Continual Few-Shot Relation ExtractorsCode0
Small Language Models as Effective Guides for Large Language Models in Chinese Relation Extraction—0
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction—0
Combining Language and Graph Models for Semi-structured Information Extraction on the Web—0
How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?—0
A Simple but Effective Approach to Improve Structured Language Model Output for Information ExtractionCode1
Creating a Fine Grained Entity Type Taxonomy Using LLMs—0
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
C-ICL: Contrastive In-context Learning for Information Extraction—0
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language ModelsCode0
Building Contextual Knowledge Graphs for Personalized Learning Recommendations using Text Mining and Semantic Graph Completion—0
Revisiting Document-Level Relation Extraction with Context-Guided Link PredictionCode0
Mining experimental data from Materials Science literature with Large Language Models: an evaluation studyCode0
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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