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

Julie Lassébie, Luca Marcolin, Marieke Vandeweyer and Benjamin Vignal

No 263, OECD Social, Employment and Migration Working Papers from OECD Publishing

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.

JEL-codes: C45 C55 J23 J24 J63 (search for similar items in EconPapers)
Date: 2021-11-11
New Economics Papers: this item is included in nep-big, nep-cmp and nep-lma
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Persistent link: https://EconPapers.repec.org/RePEc:oec:elsaab:263-en

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