Two-Step Occupation Coding

• URL: http://arxiv.org/abs/2607.20101v1
• Date: Wed, 22 Jul 2026 12:55:34 GMT
• Title: Two-Step Occupation Coding
• Abstract summary: This paper presents a novel two-step approach that separates job titles and occupational codes.<n>In the first step, a domain-specific Named Entity Recognition (NER) model identifies occupational titles in continuous text.<n>In the second step, the extracted job titles are mapped to a taxonomy, enabling the classifier to focus exclusively on this mapping.<n>The method has been developed for German documents but is transferable to other languages.
• Score: 45.88028371034407
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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