Entity and relationship extraction based on span contribution evaluation and focusing framework

Abstract

Entity and relationship extraction involves identifying named entities and extracting relationships between them. Existing research focuses on enhancing span representations, yet overlooks the impact of non-target spans(ie, the span is non-entity or the span pair has no relationship) on model training. In this work, we propose a span contribution evaluation and focusing framework named CEFF, which assigns a contribution score to each non-target span in a sentence through pre-training, which reflects the contribution of span to model performance improvement. To a certain extent, this method considers the impact of different spans on model training, making the training more targeted. Additionally, leveraging the contribution scores of non-target spans, we introduce a simplified variant of the model, termed CEFF${}_s$, which achieves comparable performance to models trained with all spans while utilizing fewer spans. This approach reduces training costs and improves training efficiency. Through extensive validation, we demonstrate that our contribution scores accurately reflect span contributions and achieve state-of-the-art results on five benchmark datasets.