🤖 AI Summary
This work addresses the challenging task of mapping occupational titles from free-form text to standardized classification schemes, particularly under the adverse effects of OCR-induced noise. To tackle this problem, the authors propose a two-stage decoupled architecture: first, a domain-adapted named entity recognition (NER) model precisely extracts occupational titles, and second, these extracted titles are mapped to the target taxonomy. This separation enables each stage to focus on a single, well-defined objective, substantially improving accuracy, robustness, and interpretability. The study further introduces an innovative margin-based confidence criterion—replacing conventional absolute thresholds—to refine mapping decisions. Experiments on German-language documents demonstrate that the proposed approach significantly outperforms end-to-end single-step baselines and exhibits strong potential for cross-lingual transfer. The implementation code and evaluation scripts are publicly released to facilitate reproducibility.
📝 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.