  Much of named entity recognition (NER) research focuses on developing
dataset-specific models based on data from the domain of interest, and a
limited set of related entity types. This is frustrating as each new dataset
requires a new model to be trained and stored. In this work, we present a
``versatile'' model -- the Prompting-based Unified NER system (PUnifiedNER) --
that works with data from different domains and can recognise up to 37 entity
types simultaneously, and theoretically it could be as many as possible. By
using prompt learning, PUnifiedNER is a novel approach that is able to jointly
train across multiple corpora, implementing intelligent on-demand entity
recognition. Experimental results show that PUnifiedNER leads to significant
prediction benefits compared to dataset-specific models with impressively
reduced model deployment costs. Furthermore, the performance of PUnifiedNER can
achieve competitive or even better performance than state-of-the-art
domain-specific methods for some datasets. We also perform comprehensive pilot
and ablation studies to support in-depth analysis of each component in
PUnifiedNER.

Computation and Language;Artificial Intelligence