garNER at SemEval-2023 : simplified knowledge augmentation for multilingual complex named entity recognition

Abstract

This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We propose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the entities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity taggers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named entities, but detrimental when the contexts are irrelevant.