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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 31–40 of 87 papers

TitleStatusHype
Joint Entity and Relation Extraction Based on Table Labeling Using Convolutional Neural NetworksCode1
A sequence-to-sequence approach for document-level relation extractionCode1
AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First -- Using Relation Extraction to Identify EntitiesCode0
OneRel:Joint Entity and Relation Extraction with One Module in One Step—0
Improved Decomposition Strategy for Joint Entity and Relation Extraction—0
Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representationsCode0
Seq2rel: A sequence-to-sequence-based approach for document-level relation extraction—0
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction—0
TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and RelationsCode1
Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based ResultsCode1
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