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Found 9 papers, 3 papers with code
BLAR: Biomedical Local Acronym Resolver
NLP has emerged as an essential tool to extract knowledge from the exponentially increasing volumes of biomedical texts.
READ: Improving Relation Extraction from an ADversarial Perspective
This strategy enables a larger attack budget for entities and coaxes the model to leverage relational patterns embedded in the context.
Creating a Discipline-specific Commons for Infectious Disease Epidemiology
Materials and Methods: We represented 586 datasets, 54 software, and 24 data formats in OWL 2 and then used logical queries to infer potentially interoperable combinations of software and datasets, as well as statistics about the FAIRness of the collection.
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase
DAIL leverages the intuition that large language models are more familiar with the content generated by themselves.
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting
Motivated by these insights, we present a novel method called KNoRD (Known and Novel Relation Discovery), which effectively classifies explicitly and implicitly expressed relations from known and novel classes within unlabeled data.
An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods
Relation Extraction (RE) is a foundational task of natural language processing.
Fine-grained Contrastive Learning for Relation Extraction
Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels.
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Relation Extraction
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Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction
In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types.
Applications of artificial intelligence in drug development using real-world data
The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development.
