IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases
This addresses the problem of improving NER accuracy for complex and new entities in deployed systems, particularly for low-resource languages, though it is incremental as it builds on existing knowledge base methods.
The paper tackles the challenge of fine-grained and emerging entity classification in Named Entity Recognition by introducing a three-step cascade approach that uses knowledge bases for entity linking and category prediction, achieving robust performance in the MultiCoNER2 shared task, including in low-resource language settings.
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.