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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 71–80 of 87 papers

TitleStatusHype
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
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple ExtractionCode0
JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their DescriptionsCode0
Distantly-Supervised Joint Extraction with Noise-Robust LearningCode0
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representationsCode0
End-to-End Temporal Relation Extraction in the Clinical DomainCode0
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