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.

通过分类框架和深度学习模型，该论文提出一种方法来识别职位要求和横向技能的关联，并预测个体职位描述所需的技能。该方法使用 ESCO（欧洲技能、能力和职业）分类法进行数据收集、预处理和标记。采用分层分类和多标签策略进行技能识别，通过数据平衡增强模型的鲁棒性。通过比较使用英语特定和多语言句子嵌入模型得到的结果，发现它们具有相近的准确性。实验案例研究详细介绍了神经网络配置、超参数和交叉验证结果，突出了分层方法的有效性和多语言模型在多样化欧洲职位市场的适用性。因此，提出了一种新的方法来从职位广告中对横向技能进行分层分类。