Rule-based detection of access to education and training in Germany
This work addresses the need to match training seekers with offers in Germany's labor market, but it is incremental as it builds on existing methods with specific adaptations.
The paper tackles the problem of automatically detecting access requirements for education and training in German advertisements by developing a rule-based approach that maps synonyms and matches qualifications, achieving promising results on two datasets of training and retraining advertisements.
As a result of transformation processes, the German labor market is highly dependent on vocational training, retraining and continuing education. To match training seekers and offers, we present a novel approach towards the automated detection of access to education and training in German training offers and advertisements. We will in particular focus on (a) general school and education degrees and schoolleaving certificates, (b) professional experience, (c) a previous apprenticeship and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide a mapping of synonyms in education combining different qualifications and adding deprecated terms. Second, we provide a rule-based matching to identify the need for professional experience or apprenticeship. However, not all access requirements can be matched due to incompatible data schemata or non-standardizes requirements, e.g initial tests or interviews. While we can identify several shortcomings, the presented approach offers promising results for two data sets: training and re-training advertisements.