Example-Based Named Entity Recognition

2020/08/24 by Ziyadi, Morteza, Sun, Yuting, Goswami, Abhishek +2
#Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · doi:10.48550/arxiv.2008.10570

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.

Related

• Few-Shot Named Entity Recognition: A Comprehensive Study
• Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
• Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER
• Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models
• Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study