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Medical Relation Extraction

Biomedical relation extraction is the task of detecting and classifying semantic relationships from biomedical text.

Papers

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Showing 1–15 of 15 papers

TitleStatusHype
LinkBERT: Pretraining Language Models with Document LinksCode2
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
BioBERT: a pre-trained biomedical language representation model for biomedical text miningCode1
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking DatasetsCode1
A Bidirectional Tree Tagging Scheme for Joint Medical Relation Extraction—0
Causal Tree Extraction from Medical Case Reports: A Novel Task for Experts-like Text Comprehension—0
Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution—0
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction—0
GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records—0
Medical Relation Extraction with Manifold Models—0
Leveraging Dependency Forest for Neural Medical Relation ExtractionCode0
Crowdsourcing Ground Truth for Medical Relation ExtractionCode0
Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory NetworkCode0
A hybrid deep learning approach for medical relation extractionCode0
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency ForestCode0
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All datasetsDDI extraction 2013 corpusCMeIE

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioLinkBERT (large)F183.35—Unverified
2NCBI_BERT(large) (P)F179.9—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-wwm-ext-largeMicro F155.9—Unverified