Background: Few-shot learning (FSL) is a class of machine learning methods that require
small numbers of labeled instances for training. With many medical topics having limited …

Prompt-based methods have been successfully applied in sentence-level few-shot learning
tasks, mostly owing to the sophisticated design of templates and label words. However …

Recently, considerable literature has grown up around the theme of few-shot named entity
recognition (NER), but little published benchmark data specifically focused on the practical …

We present a simple few-shot named entity recognition (NER) system based on nearest
neighbor learning and structured inference. Our system uses a supervised NER model …

Large language models have exhibited emergent abilities, demonstrating exceptional
performance across diverse tasks for which they were not explicitly trained, including those …

This paper presents an empirical study to efficiently build named entity recognition (NER)
systems when a small amount of in-domain labeled data is available. Based upon recent …

Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for
training. As many medical topics have limited annotated textual data in practical settings …

Electronic health record systems are ubiquitous and the majority of patients' data are now
being collected electronically in the form of free text. Deep learning has significantly …

We propose a framework for training sequence tagging models with weak supervision
consisting of multiple heuristic rules of unknown accuracy. In addition to supporting rules …

When the first transformer-based language models were published in the late 2010s,
pretraining with general text and then fine-tuning the model on a task-specific dataset often …