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Joint Entity and Relation Extraction

Joint Entity and Relation Extraction is the task of extracting entity mentions and semantic relations between entities from unstructured text with a single model.

Papers

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Showing 51–75 of 87 papers

TitleStatusHype
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction—0
Enhancing Knowledge Graph Construction Using Large Language Models—0
OneRel:Joint Entity and Relation Extraction with One Module in One Step—0
Entity Relative Position Representation based Multi-head Selection for Joint Entity and Relation Extraction—0
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results—0
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes—0
GenerativeRE: Incorporating a Novel Copy Mechanism and Pretrained Model for Joint Entity and Relation Extraction—0
Span-based Joint Entity and Relation Extraction with Attention-based Span-specific and Contextual Semantic Representations—0
Improved Decomposition Strategy for Joint Entity and Relation Extraction—0
Query-based Instance Discrimination Network for Relational Triple Extraction—0
Interim Report on Human-Guided Adaptive Hyperparameter Optimization with Multi-Fidelity Sprints—0
基于平行交互注意力网络的中文电子病历实体及关系联合抽取(Parallel Interactive Attention Network for Joint Entity and Relation Extraction Based on Chinese Electronic Medical Record)—0
Span-based joint entity and relation extraction augmented with sequence tagging mechanism—0
STable: Table Generation Framework for Encoder-Decoder Models—0
Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement—0
REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction—0
A Multi-Gate Encoder for Joint Entity and Relation Extraction—0
Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction—0
Joint extraction of entities and overlapping relations using position-attentive sequence labeling—0
A Two-Phase Paradigm for Joint Entity-Relation Extraction—0
90% F1 Score in Relational Triple Extraction: Is it Real ?—0
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation ExtractionCode0
Adversarial training for multi-context joint entity and relation extractionCode0
Joint Extraction of Entities and Relations Based on a Novel Tagging SchemeCode0
SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction TasksCode0
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