基于BERT微调与领域词典的简历信息抽取OA

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.