Example-Based Named Entity Recognition

TL;DR

This paper introduces an example-based, train-free few-shot learning method for named entity recognition that excels in low-data scenarios and adapts to new domains effectively.

Contribution

It proposes a novel train-free, few-shot NER approach inspired by question-answering, improving performance with minimal support examples.

Findings

Significantly outperforms current state-of-the-art in low-data settings

Effective in identifying entities in unseen domains

Requires fewer support examples for accurate NER

Abstract

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.