Two-Step Occupation Coding

arXiv:2607.20101 · cs.CL, cs.IR · Submitted 2026-07-22 · Read on arXiv

cs.CL, cs.IR

Submitted: 2026-07-22

Updated: 2026-07-22

Comments: Preprint of the paper accepted for the Federated Conference on Computer Science and Information Systems (FedCSIS 2026)

Journal ref: Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS), IEEE, 2026

DOI: 10.15439/2026F3041

Code: https://github.com/TM4VETR/two_step_occupation_coding

License: http://creativecommons.org/licenses/by-nc-nd/4.0/

The gist: Occupation coding links job titles in free text to occupational taxonomies and is a core task in labor market research.

Terminology

Accuracy — 19× in this paper · explained in 1 episode
F1 Score — 16× in this paper · explained in 3 episodes
Named Entity Recognition — 11× in this paper
Robustness — 10× in this paper · explained in 5 episodes
Transformer — 9× in this paper · explained in 2 episodes
Fine-Tuning — 8× in this paper · explained in 5 episodes
Interpretability — 5× in this paper · explained in 15 episodes
Core — 4× in this paper · explained in 2 episodes
Embeddings — 4× in this paper · explained in 2 episodes
Noise-aware Training — 4× in this paper
Ontology — 4× in this paper · explained in 2 episodes
Benchmark — 3× in this paper · explained in 2 episodes
Transfer Learning — 3× in this paper · explained in 10 episodes
Generative AI — 2× in this paper · explained in 2 episodes
Named Entity Recognition (NER) — 2× in this paper · explained in 2 episodes
Alignment — 1× in this paper · explained in 1 episode
Calibration — 1× in this paper · explained in 7 episodes
Consistency — 1× in this paper · explained in 3 episodes
Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
International Standard Classification of Occupations1 — 1× in this paper
Loss Function — 1× in this paper · explained in 2 episodes
Optical Character Recognition — 1× in this paper
Semantic Understanding — 1× in this paper · explained in 2 episodes
Systematic Bias — 1× in this paper · explained in 3 episodes
Tokenization — 1× in this paper · explained in 2 episodes
Transformer Architecture — 1× in this paper · explained in 2 episodes

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.

Two-Step Occupation Coding

Terminology

Abstract

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