Occupational models from 42 million unstructured job postings.
SOC codes
employment services
job descriptions
job titles
labor markets
natural language processing
occupational hazards
remote work
skills
Journal
Patterns (New York, N.Y.)
ISSN: 2666-3899
Titre abrégé: Patterns (N Y)
Pays: United States
ID NLM: 101767765
Informations de publication
Date de publication:
14 Jul 2023
14 Jul 2023
Historique:
received:
16
11
2022
revised:
10
01
2023
accepted:
24
04
2023
medline:
31
7
2023
pubmed:
31
7
2023
entrez:
31
7
2023
Statut:
epublish
Résumé
Structuring jobs into occupations is the first step for analysis tasks in many fields of research, including economics and public health, as well as for practical applications like matching job seekers to available jobs. We present a data resource, derived with natural language processing techniques from over 42 million unstructured job postings in the National Labor Exchange, that empirically models the associations between occupation codes (estimated initially by the Standardized Occupation Coding for Computer-assisted Epidemiological Research method), skill keywords, job titles, and full-text job descriptions in the United States during the years 2019 and 2021. We model the probability that a job title is associated with an occupation code and that a job description is associated with skill keywords and occupation codes. Our models are openly available in the
Identifiants
pubmed: 37521040
doi: 10.1016/j.patter.2023.100757
pii: S2666-3899(23)00102-2
pmc: PMC10382938
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100757Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
M.H. is currently senior data scientist at Amazon.com, Inc., but conducted this research prior to starting that role.
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