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