分两步识别职业名称并映射编码，提升准确性和可解释性。
Two-Step Occupation Coding
- 先用领域专用命名实体识别提取职业名，再映射到分类体系
- 在德语文档上比单步方法准确率提升，对文本噪声更鲁棒
- 提供置信度判据和开源代码，适合劳动市场分析研究者
职业编码将自由文本中的职位名称关联到职业分类体系，是劳动力市场研究的核心任务。现有方法通常采用端到端单一步骤，同时识别职位名称并分配职业代码。本文提出一种新型两步法，将两个任务分离：第一步使用领域特定的命名实体识别（NER）模型从连续文本中提取职业名称，即使在包含光学字符识别错误等噪声的情况下也能有效工作；第二步将提取出的职业名称映射到分类体系，使分类器可专注于此映射任务。实验表明，该分离方法相比单步方法显著提升了准确性、鲁棒性和可解释性。该方法针对德语文档开发，但具备跨语言可迁移性。此外，我们引入基于间隔的置信度判据，替代常用的绝对阈值。为支持可复现性，已公开源代码与评估脚本。
原文摘要 · Abstract (English)
Occupation coding links job titles in free text to occupational taxonomies and is a core task in labor market research. Existing approaches typically address this problem in a single end-to-end step, jointly identifying job titles and assigning occupational codes. This paper presents a novel two-step approach that separates these tasks. In the first step, a domain-specific Named Entity Recognition (NER) model identifies occupational titles in continuous text, even under noise such as OCR errors. In the second step, the extracted job titles are mapped to a taxonomy, enabling the classifier to focus exclusively on this mapping. We demonstrate that this separation improves accuracy, robustness, and interpretability compared to single-step approaches. The method has been developed for German documents but is transferable to other languages. We further introduce a margin-based confidence criterion for occupation coding, replacing common absolute thresholds. To support reproducibility, we publish the source code and evaluation scripts.
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