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

• URL: http://arxiv.org/abs/2401.05073v1
• Date: Wed, 10 Jan 2024 11:07:32 GMT
• Title: Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings
• Authors: Florin Leon, Marius Gavrilescu, Sabina-Adriana Floria, Alina-Adriana Minea
• Abstract summary: This paper aims to identify correlations between job ad requirements and skill sets using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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