郑州大学学报（理学版）2025，Vol.57Issue(5)：31-38,8.DOI:10.13705/j.issn.1671-6841.2024040
一种混合提示学习与规则的领域命名实体识别方法
A Hybrid Approach of Prompt-based Learning and Rules for Domain Specific Named Entity Recognition
摘要
Abstract
Prompt-based fine-tuning was a new direction to improve the performance of domain specific named entity recognition(NER).However,the existing methods faced challenges such as the need of manual template construction,lengthy prompt information,and fixed prompt templates.To address these issues,a method combined prompt learning with expert knowledge was proposed in the field of domain specific named entity recognition.Firstly,by introducing the bootstrapping algorithm,potential entities were automatically identified.And the string matching algorithm used in the process of obtaining unanno-tated entity types from the same context was improved to obtain more prompt information templates.Next,expert knowledge from the domain ontology was introduced to address the reliability concerns associated with prompt information.Simultaneously,first-order predicate logic was used to represent prompt infor-mation and to improve the representation of prompt information.Finally,with experiments on finance dataset and information security dataset,the method was verified to improve the performance of domain specific named entity recognition effectively.关键词
提示学习/命名实体识别/自然语言处理/低资源Key words
prompt based learning/named entity recognition/natural language processing/low resource分类
信息技术与安全科学引用本文复制引用
张晗,张亚洲,徐秉智,张铖方..一种混合提示学习与规则的领域命名实体识别方法[J].郑州大学学报（理学版）,2025,57(5):31-38,8.基金项目
智能警务四川省重点实验室开放课题(ZNJW2024KFQN005) （ZNJW2024KFQN005）
河南省高等学校重点科研项目(24A520047) （24A520047）
河南省重大科技专项(231100210200) （231100210200）
