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Taxonomy-based vacancy : CV matching
The changes in the labor market accompanied by digitalization have influenced the approaches used for recruitment purposes. The development of machine learning and, specifically, natural language processing allowed new services for automated resume screening to appear. These services aim to reduce the time that HR specialists dedicate to sorting out unsuitable candidates, thus saving the costs per hire. This master thesis provides an analysis of the possible approach for matching CVs to JDs based on the taxonomy of competencies. In the literature review, there are the description and comparison of the different ontologies and taxonomies, and the analysis of the previous research performed in vacancy–CV matching provided. In the methodology part, there are the concepts of natural language processing and information extraction described, and the Sentence Transformer model and named entity recognition explained. Besides, evaluation approaches for taxonomy enrichment and vacancy–CV matching are suggested. In the solution development part, the described concepts and models are applied to the JDs and CVs data to perform taxonomy enrichment and matching tasks. The assessment of the matching algorithm performance based on the expert’s evaluation is presented. Based on the results obtained, the potential usage of the research and possible limitations are discussed.
information extraction, natural language processing, SBERT model, Sentence Transformer, NER, job descriptions analysis, skills taxonomy, fi=Datatiede|en=Data science|, fi=School of Business and Management, Kauppatieteet|en=School of Business and Management, Business Administration|
information extraction, natural language processing, SBERT model, Sentence Transformer, NER, job descriptions analysis, skills taxonomy, fi=Datatiede|en=Data science|, fi=School of Business and Management, Kauppatieteet|en=School of Business and Management, Business Administration|
The changes in the labor market accompanied by digitalization have influenced the approaches used for recruitment purposes. The development of machine learning and, specifically, natural language processing allowed new services for automated resume screening to appear. These services aim to reduce the time that HR specialists dedicate to sorting out unsuitable candidates, thus saving the costs per hire. This master thesis provides an analysis of the possible approach for matching CVs to JDs based on the taxonomy of competencies. In the literature review, there are the description and comparison of the different ontologies and taxonomies, and the analysis of the previous research performed in vacancy–CV matching provided. In the methodology part, there are the concepts of natural language processing and information extraction described, and the Sentence Transformer model and named entity recognition explained. Besides, evaluation approaches for taxonomy enrichment and vacancy–CV matching are suggested. In the solution development part, the described concepts and models are applied to the JDs and CVs data to perform taxonomy enrichment and matching tasks. The assessment of the matching algorithm performance based on the expert’s evaluation is presented. Based on the results obtained, the potential usage of the research and possible limitations are discussed.