# Two-Step Occupation Coding

arXiv: https://arxiv.org/abs/2607.20101 · HTML: https://mlsanity.com/paper/2607.20101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Overwriting Pretrained Bias with Finetuning Data](https://mlsanity.com/paper/2303.06167/) (2023)
- [JobBERT: Understanding Job Titles through Skills](https://mlsanity.com/paper/2109.09605/) (2021)
- [NAT: Noise-Aware Training for Robust Neural Sequence Labeling](https://mlsanity.com/paper/2005.07162/) (2020)
- [Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling](https://mlsanity.com/paper/2105.11872/) (2021)
- [Efficient, Lexicon-Free OCR using Deep Learning](https://mlsanity.com/paper/1906.01969/) (2019)
- [Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining](https://mlsanity.com/paper/2107.07113/) (2021)
- [Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion](https://mlsanity.com/paper/2004.02814/) (2020)
- [Noise-Aware Named Entity Recognition for Historical VET Documents](https://mlsanity.com/paper/2601.00488/) (2026)

## 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.