Natural language processing in lifelong learning choices : a case of Finland
Zheleiko, Irina (2023)
Pro gradu -tutkielma
2023
School of Business and Management, Kauppatieteet
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe202301091840
https://urn.fi/URN:NBN:fi-fe202301091840
Tiivistelmä
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.
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.