Joint Entity and Relation Extraction
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Benchmarks
SciERC
WebNLG 3.0
DocRED
NYT
ACE2005
ADE Corpus
CoNLL04
WebNLG
SemEval-2022 Task-12
TekGen
ACE 2005
CDR
DocRED-IE
GDA
KPI-EDGAR
Most implemented
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
A General Framework for Information Extraction using Dynamic Span Graphs
A sequence-to-sequence approach for document-level relation extraction
Papers
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
Joint Entity and Relation Extraction (JERE) is highly sensitive to training data quality, making data augmentation a natural way to improve generalization. However, existing augmentation methods often weaken entity relev…
Joint Entity and Relation ExtractionData AugmentationTIJERE: A Novel Threat Intelligence Joint Extraction Model Based on Analyst Expert Knowledge
The extraction of entities and relationships from threat intelligence reports into structured formats, such as cybersecurity knowledge graphs, is essential for automated threat analysis, detection, and mitigation. Howeve…
Joint Entity and Relation ExtractionKnowledge GraphsDocIE@XLLM25: In-Context Learning for Information Extraction using Fully Synthetic Demonstrations
Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data g…
In-Context LearningJoint Entity and Relation ExtractionRelationRelation Extraction+1Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints
This case study applies a phased hyperparameter optimization process to compare multitask natural language model variants that utilize multiphase learning rate scheduling and optimizer parameter grouping. We employ short…
Bayesian OptimizationHyperparameter OptimizationJoint Entity and Relation ExtractionLanguage Modeling+3REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). However, existing approaches for cIE suffer …
Benchmarkingcoreference-resolutionCoreference ResolutionDocument-level Closed Information Extraction+11EnriCo: Enriched Representation and Globally Constrained Inference for Entity and Relation Extraction
Joint entity and relation extraction plays a pivotal role in various applications, notably in the construction of knowledge graphs. Despite recent progress, existing approaches often fall short in two key aspects: richne…
Joint Entity and Relation ExtractionKnowledge GraphsRelationRelation Extraction