LPNER: Label Prompt for Few-shot Nested Named Entity Recognition

Jiaoyun Yang, Zhihan Zhu, Hong Ming, Lili Jiang, Ning An

Keywords: Nested named recognition, Few-shot learning, Prompt learning, Label semantics

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TL;DR: Label-Prompt-based method for Few-shot Nested Named Entity Recognition

Abstract: Few-shot Named Entity Recognition (NER) aims to identify named entities using very little annotated data. Recently, prompt-based few-shot NER methods have demonstrated significant effectiveness. However, most existing methods employ multi-round prompts, which significantly increase time and computational costs. Furthermore, current single-round prompt methods are mainly designed for flat NER tasks and are not effective in handling nested NER tasks. Additionally, these methods do not to fully utilize the semantic information of entity labels through prompts. To address these challenges, we propose a novel Label-Prompt-based few-shot nested NER method named LPNER, which not only handles nested NER tasks but also efficiently extracts semantic information of entities through label prompts, thereby achieving more efficient and accurate NER. LPNER first designs a specialized prompt based on a span strategy to enhance label semantics and effectively combines multiple span representations using special mark to obtain enhanced span representations integrated with label semantics. Then, entity prototypes are constructed through prototype network for classifying candidate entity spans. We conducted extensive experiments on five nested datasets: ACE04, ACE05, GENIA, GermEval, and NEREL. In 1-shot and 5-shot tasks, LPNER's $F_1$ scores mostly outperform baseline models.

Primary Area: Applications (bioinformatics, biomedical informatics, climate science, collaborative filtering, computer vision, healthcare, human activity recognition, information retrieval, natural language processing, social networks, etc.)

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Submission Number: 240