Survey on entity extraction for lowresource scenarios

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: metalearning 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.

Keywords

• entity extraction;low-resource scenarios;few-shot learning