Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data

Stephen Meisenbacher, Svetlozar Nestorov, Peter Norlander
10/1/2025
cs.CYcs.CL

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Abstract

Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demonstrate its reliability and accuracy in out-of-sample and LLM-as-a-Judge testing. We extract more than 10 billion data points from more than 155 million online job ads provided by the National Labor Exchange (NLx) Research Hub, including O*NET tasks, occupation codes, tools, and technologies, as well as wages, skills, industry, and more features. We describe the construction of a dataset of occupation, state, and industry level features aggregated by monthly active jobs from 2015 - 2025. We illustrate the potential for research and future uses in education and workforce development.

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Cite this paper

@article{meisenbacher2025extracting,
  title  = {Extracting O*NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data},
  author = {Stephen Meisenbacher and Svetlozar Nestorov and Peter Norlander},
  year   = {2025},
  eprint = {2510.01470},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.01470v1}
}

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