现代信息科技2026，Vol.10Issue(11)：68-72,80,6.DOI:10.19850/j.cnki.2096-4706.2026.11.013
基于BERT微调与领域词典的简历信息抽取
Resume Information Extraction Based on BERT Fine-tuning and Domain Dictionary
钱华远1
作者信息
- 1. 河北工业大学 人工智能与数据科学学院,天津 300401
- 折叠
摘要
Abstract
In order to enhance the comprehensive performance of structured information extraction from resumes under small training sample conditions,and provide a viable approach for efficient resume screening,this paper proposes a method to generate high-quality training corpora in the absence of domain-specific annotation data by constructing a competition-specific domain dictionary and leveraging it to augment datasets from a limited number of resumes.Based on the BERT model,key information extraction is performed after training on datasets of varying augmentation scales,and evaluation metrics are calculated.The experimental results indicate that the F1 scores for information extraction on two test sets reach 91.79％and 76.47％,validating the effectiveness of the"domain dictionary+data augmentation"strategy in low-resource named entity recognition tasks.关键词
命名实体识别/自然语言处理/领域词典/数据增广/BERT模型/信息抽取Key words
Named Entity Recognition/Natural Language Processing/domain dictionary/data augmentation/BERT model/information extraction分类
信息技术与安全科学引用本文复制引用
钱华远..基于BERT微调与领域词典的简历信息抽取[J].现代信息科技,2026,10(11):68-72,80,6.
