一种混合提示学习与规则的领域命名实体识别方法

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关键词：提示学习；命名实体识别；自然语言处理；低资源

中图分类号：TP183 文献标志码：A 文章编号：1671-6841（2025）05-0031-08

DOI： 10.13705/j.issn.1671-6841.2024040

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 unannotated 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 information 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.

Key words： prompt based learning； named entity recognition； natural language processing; low resource

0 引言

命名实体识别（NER）旨在从文本中提取各种类型的实体，其结果可用于其他复杂任务诸如关系提取[1]、领域知识图谱的构建[2-3]等。（剩余14067字）