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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 26–50 of 87 papers

TitleStatusHype
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural NetworksCode1
Joint Entity and Relation Extraction with Set Prediction NetworksCode1
Knowledge Graph Generation From TextCode1
KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial DocumentsCode1
Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation ExtractionCode1
Multilingual Entity and Relation Extraction Dataset and ModelCode1
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph ConstructionCode1
Packed Levitated Marker for Entity and Relation ExtractionCode1
REBEL: Relation Extraction By End-to-end Language generationCode1
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language ModelsCode1
Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation ExtractionCode1
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity LinkingCode1
Structured Prediction as Translation between Augmented Natural LanguagesCode1
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and RelationsCode1
Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence EncodersCode1
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify EntitiesCode0
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representationsCode0
Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation ExtractionCode0
Similarity-based Memory Enhanced Joint Entity and Relation ExtractionCode0
CARE: Co-Attention Network for Joint Entity and Relation ExtractionCode0
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple ExtractionCode0
End-to-End Temporal Relation Extraction in the Clinical DomainCode0
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation ExtractionCode0
Span-based Joint Entity and Relation Extraction with Transformer Pre-trainingCode0
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify EntitiesCode0
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