Harnessing high-quality pseudo-labels for robust few-shot nested named entity recognition

Few-shot Named Entity Recognition (NER) methods have shown initial effectiveness in flat NER tasks. However, these methods often prioritize optimizing models with a small annotated support set, neglecting the high-quality data within the unlabeled query set. Furthermore, existing few-shot NER models struggle with nested entity challenges due to linguistic or structural complexities. In this study, we introduce Retrieving high-quality pseudo-label Tuning, RiTNER, a framework designed to address few-shot nested named entity recognition tasks by leveraging high-quality data from the query set. RiTNER comprises two main components: (1) contrastive span classification, which clusters entities into corresponding prototypes and generates high-quality pseudo-labels from the unlabeled data, and (2) masked pseudo-data tuning, which generates a masked pseudo dataset and then uses it to optimize the model and enhance span classification. We train RiTNER on an English dataset and evaluate it on both English nested datasets and cross-lingual nested datasets. The results show that RiTNER outperforms the top-performing baseline models by 1.67%, and 3.04% in the English 5-shot task, as well as the cross-lingual 5-shot tasks, respectively.