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

IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases