大数据2024，Vol.10Issue(1)：46-61,16.DOI:10.11959/j.issn.2096-0271.2023079
面向低资源场景的实体知识获取研究综述
Survey on entity extraction for low-resource scenarios
徐道柱 1赵凯琳 2康栋 3马超 1冯禹铭 2李紫宣 2弋步荣 3靳小龙2
作者信息
- 1. 西安测绘研究所,陕西 西安 710054
- 2. 中国科学院计算技术研究所,北京 100086
- 3. 航天恒星科技有限公司,北京 100089
- 折叠
摘要
Abstract
Entity extraction is an essential task in information extraction.In recent years,under the trend of training model with big data,deep learning has achieved success in entity extraction.However,in the fields such as natural environment,there are very few entity samples or labeled samples of terrain,disasters and other types,and labeling those unlabeled samples is time-consuming and laborious.Therefore,entity extraction for low-resource scenarios has gradually attracted more and more attention,which is called low-resource entity extraction or few-shot entity extraction.This paper systematically combs the current approaches of low-resource entity extraction.It introduces the research status of three types of methods:meta-learning based,multi-task learning based,and prompt learning based.Next,the paper summarizes the low-resource entity extraction datasets and the experimental results of the representative models on these datasets.In the following,the current low-resource entity extraction approaches are analysed.Finally,this paper summarizes the challenges of low-resource entity extraction and discusses the future research direction in this field.关键词
实体获取/低资源场景/小样本学习Key words
entity extraction/low-resource scenarios/few-shot learning分类
信息技术与安全科学引用本文复制引用
徐道柱,赵凯琳,康栋,马超,冯禹铭,李紫宣,弋步荣,靳小龙..面向低资源场景的实体知识获取研究综述[J].大数据,2024,10(1):46-61,16.
