  We present a novel approach to named entity recognition (NER) in the presence
of scarce data that we call example-based NER. Our train-free few-shot learning
approach takes inspiration from question-answering to identify entity spans in
a new and unseen domain. In comparison with the current state-of-the-art, the
proposed method performs significantly better, especially when using a low
number of support examples.
