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Two-Step Occupation Coding

arXiv 2607.20101 ↗First posted 22 Jul 2026cs.CL cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 22 Jul 2026.

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References · 17

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5 more
  1. 2021Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labelingcited by 8
  2. 2019Efficient, Lexicon-Free OCR using Deep Learningcited by 5
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