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Continual Contrastive Finetuning Improves Low-Resource Relation Extraction
Wenxuan Zhou, Sheng Zhang, Tristan Naumann +2
Relation extraction (RE), which has relied on structurally annotated corpora for model training, has been particularly challenging in low-resource scenarios and domains. Recent lit…
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur +9
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical re…
Towards Structuring Real-World Data at Scale: Deep Learning for Extracting Key Oncology Information from Clinical Text with Patient-Level Supervision
Sam Preston, Mu Wei, Rajesh Rao +11
Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and ti…