A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models  

作　　者：Shuanglong Li Xulong Zhang Jianzong Wang 

机构地区：[1]Shenzhen University,Shenzhen 518000,Guangdong Province,China [2]Ping An Technology(Shenzhen)Co.,Ltd.,Shenzhen 518000,Guangdong Province,China

出　　处：《Journal of Electronic Research and Application》2024年第5期125-133,共9页电子研究与应用

摘　　要：Named Entity Recognition(NER)is crucial for extracting structured information from text.While traditional methods rely on rules,Conditional Random Fields(CRFs),or deep learning,the advent of large-scale Pre-trained Language Models(PLMs)offers new possibilities.PLMs excel at contextual learning,potentially simplifying many natural language processing tasks.However,their application to NER remains underexplored.This paper investigates leveraging the GPT-3 PLM for NER without fine-tuning.We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity.Our experimental results demonstrate the feasibility of this approach,suggesting a promising direction for harnessing PLMs in NER.

关 键 词：GPT-3 Named Entity Recognition Sentence-BERT model In-context example 

分 类 号：H31[语言文字—英语]