  Named Entity Recognition (NER) is a core natural language processing task in
which pre-trained language models have shown remarkable performance. However,
standard benchmarks like CoNLL 2003 do not address many of the challenges that
deployed NER systems face, such as having to classify emerging or complex
entities in a fine-grained way. In this paper we present a novel NER cascade
approach comprising three steps: first, identifying candidate entities in the
input sentence; second, linking the each candidate to an existing knowledge
base; third, predicting the fine-grained category for each entity candidate. We
empirically demonstrate the significance of external knowledge bases in
accurately classifying fine-grained and emerging entities. Our system exhibits
robust performance in the MultiCoNER2 shared task, even in the low-resource
language setting where we leverage knowledge bases of high-resource languages.
