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arXiv · 2401.05073

Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings

Florin Leon·Marius Gavrilescu·Sabina-Adriana Floria·Alina-Adriana Minea
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Abstract

This paper proposes a classification framework aimed at identifying correlations between job ad requirements and transversal skill sets, with a focus on predicting the necessary skills for individual job descriptions using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy. Hierarchical classification and multi-label strategies are used for skill identification, while augmentation techniques address data imbalance, enhancing model robustness. A comparison between results obtained with English-specific and multi-language sentence embedding models reveals close accuracy. The experimental case studies detail neural network configurations, hyperparameters, and cross-validation results, highlighting the efficacy of the hierarchical approach and the suitability of the multi-language model for the diverse European job market. Thus, a new approach is proposed for the hierarchical classification of transversal skills from job ads.

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Florin Leon, Marius Gavrilescu, Sabina-Adriana Floria, Alina-Adriana Minea. 2024-01-10. Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings. https://doi.org/10.3390/info15030151

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