Abstract
This paper addresses Named Entity Recognition (NER) in the domain of Vocational Education and Training (VET), focusing on historical, digitized documents that suffer from OCR-induced noise. We propose a robust NER approach leveraging Noise-Aware Training (NAT) with synthetically injected OCR errors, transfer learning, and multi-stage fine-tuning. Three complementary strategies, training on noisy, clean, and artificial data, are systematically compared. Our method is one of the first to recognize multiple entity types in VET documents. It is applied to German documents but transferable to arbitrary languages. Experimental results demonstrate that domain-specific and noise-aware fine-tuning substantially increases robustness and accuracy under noisy conditions. We provide publicly available code for reproducible noise-aware NER in domain-specific contexts.
Abstract (translated)
本文研究了职业教育与培训（VET）领域中的命名实体识别（NER），重点关注因光学字符识别（OCR）导致噪声的历史、数字化文档。我们提出了一种稳健的NER方法，利用带有合成注入OCR错误的Noise-Aware Training (NAT)、迁移学习和多阶段微调技术。文中系统地比较了三种互补策略：在有噪音数据、干净数据以及人工生成的数据上进行训练的效果。我们的方法是首次能够在VET文档中识别多种实体类型的模型，适用于德国文件但也可应用于任意语言的文本。实验结果表明，在噪声条件下，领域特定且具有噪声意识的微调显著提高了鲁棒性和准确性。我们提供了公开可用的代码以实现可重复的领域特异性噪声感知NER研究。
URL
https://arxiv.org/abs/2601.00488
