Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data
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 a...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Paris
OECD Publishing
2021
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| Schriftenreihe: | OECD Social, Employment and Migration Working Papers
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| Schlagworte: | |
| Online-Zugang: | kostenfrei |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (51 Seiten) 21 x 28cm |
| DOI: | 10.1787/adb03746-en |
Internformat
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| index_date | 2024-07-03T19:34:54Z |
| indexdate | 2024-12-18T19:02:01Z |
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| spelling | Lassébie, Julie Verfasser aut Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data Julie Lassébie ... [et al] Paris OECD Publishing 2021 1 Online-Ressource (51 Seiten) 21 x 28cm txt rdacontent c rdamedia cr rdacarrier OECD Social, Employment and Migration Working Papers 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 Social Issues/Migration/Health Employment Marcolin, Luca ctb Vandeweyer, Marieke ctb Vignal, Benjamin ctb https://doi.org/10.1787/adb03746-en Verlag kostenfrei Volltext |
| spellingShingle | Lassébie, Julie Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data Social Issues/Migration/Health Employment |
| title | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data |
| title_auth | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data |
| title_exact_search | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data |
| title_exact_search_txtP | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data |
| title_full | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data Julie Lassébie ... [et al] |
| title_fullStr | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data Julie Lassébie ... [et al] |
| title_full_unstemmed | Speaking the same language A machine learning approach to classify skills in Burning Glass Technologies data Julie Lassébie ... [et al] |
| title_short | Speaking the same language |
| title_sort | speaking the same language a machine learning approach to classify skills in burning glass technologies data |
| title_sub | A machine learning approach to classify skills in Burning Glass Technologies data |
| topic | Social Issues/Migration/Health Employment |
| topic_facet | Social Issues/Migration/Health Employment |
| url | https://doi.org/10.1787/adb03746-en |
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