2025-01-14-yang25a.md

title	LPNER: Label Prompt for Few-shot Nested Named Entity Recognition
booktitle	Proceedings of the 16th Asian Conference on Machine Learning
year	2025
volume	260
series	Proceedings of Machine Learning Research
month	0
publisher	PMLR
pdf	https://raw.githubusercontent.com/mlresearch/v260/main/assets/yang25a/yang25a.pdf
url	https://proceedings.mlr.press/v260/yang25a.html
openreview	C3W2YJf4uW
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.
layout	inproceedings
issn	2640-3498
id	yang25a
tex_title	{LPNER}: {L}abel Prompt for Few-shot Nested Named Entity Recognition
firstpage	781
lastpage	796
page	781-796
order	781
cycles	false
bibtex_editor	Nguyen, Vu and Lin, Hsuan-Tien
editor	given family Vu Nguyen given family Hsuan-Tien Lin
bibtex_author	Yang, Jiaoyun and Zhu, Zhihan and Ming, Hong and Jiang, Lili and An, Ning
author	given family Jiaoyun Yang given family Zhihan Zhu given family Hong Ming given family Lili Jiang given family Ning An
date	2025-01-14
address
container-title	Proceedings of the 16th Asian Conference on Machine Learning
genre	inproceedings
issued	date-parts 2025 1 14
extras