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  • 2023-2023
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  • Open Access English
    Authors: 
    Zheleiko, Irina;
    Country: Finland

    Such techniques of Natural Language Processing as information extraction and semantic text labelling had been widely utilised in recruitment sphere to decrease the labour and time resources needed to analyse CVs or labour market’s trends. However, the application of such techniques and establishing link between demand for the workforce and education providing organizations is yet to be established. In the current thesis the ideas on processing educational courses descriptions texts is provided in attempt to facilitate the information exchange between the needs of the labour market and skills supply from the educational establishments. In the literature review the analysis of the most recent methods in natural language processing methods is provided (Word2Vec, NER, Sentence Transformers) as well as commentary on their current implementations in labour market related spheres. In the empirical section state-of-the-art SBERT language model is applied to the collected open university courses’ descriptions in order to extract concrete skills from the and then the performance of the SBERT model is accessed through such metrics as precision, recall and f-score, yielding the F-score of 70.4%. As a result, an example of comparison between the skills supplies as identified by Finnish open universities educational courses and demand as identified by the job descriptions data is provided. In conclusion, research paper’s possible managerial applications and theoretical contribution are included.

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Advanced search in Research products
Research products
arrow_drop_down
Searching FieldsTerms
Any field
arrow_drop_down
includes
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Include:
The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
1 Research products, page 1 of 1
  • Open Access English
    Authors: 
    Zheleiko, Irina;
    Country: Finland

    Such techniques of Natural Language Processing as information extraction and semantic text labelling had been widely utilised in recruitment sphere to decrease the labour and time resources needed to analyse CVs or labour market’s trends. However, the application of such techniques and establishing link between demand for the workforce and education providing organizations is yet to be established. In the current thesis the ideas on processing educational courses descriptions texts is provided in attempt to facilitate the information exchange between the needs of the labour market and skills supply from the educational establishments. In the literature review the analysis of the most recent methods in natural language processing methods is provided (Word2Vec, NER, Sentence Transformers) as well as commentary on their current implementations in labour market related spheres. In the empirical section state-of-the-art SBERT language model is applied to the collected open university courses’ descriptions in order to extract concrete skills from the and then the performance of the SBERT model is accessed through such metrics as precision, recall and f-score, yielding the F-score of 70.4%. As a result, an example of comparison between the skills supplies as identified by Finnish open universities educational courses and demand as identified by the job descriptions data is provided. In conclusion, research paper’s possible managerial applications and theoretical contribution are included.

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