  Named-entity recognition (NER) detects texts with predefined semantic labels
and is an essential building block for natural language processing (NLP).
Notably, recent NER research focuses on utilizing massive extra data, including
pre-training corpora and incorporating search engines. However, these methods
suffer from high costs associated with data collection and pre-training, and
additional training process of the retrieved data from search engines. To
address the above challenges, we completely frame NER as a machine reading
comprehension (MRC) problem, called NER-to-MRC, by leveraging MRC with its
ability to exploit existing data efficiently. Several prior works have been
dedicated to employing MRC-based solutions for tackling the NER problem,
several challenges persist: i) the reliance on manually designed prompts; ii)
the limited MRC approaches to data reconstruction, which fails to achieve
performance on par with methods utilizing extensive additional data. Thus, our
NER-to-MRC conversion consists of two components: i) transform the NER task
into a form suitable for the model to solve with MRC in a efficient manner; ii)
apply the MRC reasoning strategy to the model. We experiment on 6 benchmark
datasets from three domains and achieve state-of-the-art performance without
external data, up to 11.24% improvement on the WNUT-16 dataset.
