IDEAS home Printed from https://ideas.repec.org/p/oec/elsaab/263-en.html
   My bibliography  Save this paper

Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data

Author

Listed:
  • Julie Lassébie
  • Luca Marcolin
  • Marieke Vandeweyer
  • Benjamin Vignal

Abstract

This report presents a methodology to classify skill requirements in online job postings into a pre-existing expert-driven taxonomy of broader skill categories. The proposed approach uses a semi-supervised Machine Learning algorithm and relies on the actual meaning and definition of the skills. It allows for the classification of more than 17 000 unique skill keywords contained in the Burning Glass dataset into 61 categories. The outcome of the classification exercise is validated using O*NET information on skills by occupations, and by benchmarking the results of some empirical descriptive exercises against the existing literature. Compared to a manual classification, the proposed approach organises large amounts of skills information in an analytically tractable form, and with considerable savings in time and human resources.

Suggested Citation

  • Julie Lassébie & Luca Marcolin & Marieke Vandeweyer & Benjamin Vignal, 2021. "Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data," OECD Social, Employment and Migration Working Papers 263, OECD Publishing.
    • Handle: RePEc:oec:elsaab:263-en
    DOI: 10.1787/adb03746-en
    as

    Download full text from publisher

    File URL: https://doi.org/10.1787/adb03746-en
    Download Restriction: no
    File URL: https://libkey.io/10.1787/adb03746-en?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Julia Darby & Stuart McIntyre & Graeme Roy, 2022. "What can analysis of 47 million job advertisements tell us about how opportunities for homeworking are evolving in the United Kingdom?," Industrial Relations Journal, Wiley Blackwell, vol. 53(4), pages 281-302, July.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oec:elsaab:263-en. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/eloecfr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.