Automated classification of German job titles according to KldB : challenges and novel methods

Abstract

"The automated classification of job titles constitutes a critical component of labor market research, survey analysis, and administrative data processing. The present study explores the classification of German job titles according to the German Classification of Occupations (KldB), with a particular emphasis on the linguistic and structural challenges that are inherent to this task. This study builds upon previous research by incorporating a variety of heterogeneous data sources, including manually annotated survey responses, a comprehensive synonym dataset, online job advertisements (OJAs), and vocational education and training titles from DAZUBI. Conventional machine learning models, including logistic regression, naive Bayes, and random forest, are employed to assess the classification performance at varying taxonomic levels of the KldB. The findings of the present study demonstrate that while substantial results can be achieved for broad occupational categories, fine-grained classification, particularly at the level of performance (5th digit), remains challenging. The findings underscore the limitations of relying solely on job titles and underscore the importance of richer contextual information and more expressive models. This work provides both an expanded dataset and a systematic analysis of classification performance, thereby establishing the foundation for future research on context-aware occupational coding in the German labor market." (Authors' abstract, BIBB-Doku)