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Relation Extraction On Ade Corpus

Metrics

NER Macro F1
RE+ Macro F1

Results

Performance results of various models on this benchmark

Paper Title
ITER92.63 ± 0.8985.6 ± 1.42ITER: Iterative Transformer-based Entity Recognition and Relation Extraction
PFN (ALBERT XXL, average aggregation)91.583.9An Information Extraction Study: Take In Mind the Tokenization!
Deeper89.4883.74Deeper Task-Specificity Improves Joint Entity and Relation Extraction
PFN (ALBERT XXL, no aggregation)91.383.2A Partition Filter Network for Joint Entity and Relation Extraction
SpERT.PL (without overlap and BioBERT)91.1482.39Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types
REBEL (including overlapping entities)-82.2REBEL: Relation Extraction By End-to-end Language generation
SpERT.PL (with overlap and BioBERT)91.1782.03Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types
CMAN89.4081.14Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
Table-Sequence89.780.1Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
CLDR + CLNER88.379.97Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning
SpERT (without overlap)89.2579.24Span-based Joint Entity and Relation Extraction with Transformer Pre-training
SpERT (with overlap)89.2878.84Span-based Joint Entity and Relation Extraction with Transformer Pre-training
Relation-Metric87.0277.19Neural Metric Learning for Fast End-to-End Relation Extraction
multi-head + AT86.7375.52Adversarial training for multi-context joint entity and relation extraction
multi-head86.4074.58Joint entity recognition and relation extraction as a multi-head selection problem
0 of 15 row(s) selected.
Relation Extraction On Ade Corpus | SOTA | HyperAI