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

TL;DR

This paper introduces a three-step cascade approach for multilingual NER that leverages external knowledge bases to improve classification of fine-grained and emerging entities, especially in low-resource languages.

Contribution

The novel NER cascade method integrates candidate detection, knowledge base linking, and fine-grained classification, demonstrating improved accuracy with external knowledge bases.

Findings

External knowledge bases significantly improve entity classification accuracy.

The system performs robustly in low-resource language settings.

The approach outperforms baseline models in the MultiCoNER2 shared task.

Abstract

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.