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.

翻译：由于转型进程的影响，德国劳动力市场高度依赖职业培训、转岗培训和继续教育。为实现培训需求方与供给方的匹配，我们提出了一种新颖的自动化检测方法，用于识别德国培训项目及招聘广告中的教育培训准入要求。本研究特别关注以下四类要求：（a）普通学校教育学位及毕业证书，（b）职业经验，（c）既往学徒经历，（d）德国联邦就业局提供的技能清单。该方法融合多种技术：首先，构建教育领域同义词映射，整合不同资质并纳入已废弃术语；其次，通过基于规则的匹配识别职业经验或学徒经历需求。然而，受数据模式不兼容或非标准化要求（如入职测试或面试）限制，并非所有准入条件均可匹配。尽管存在若干不足，本方法在培训广告与再培训广告两个数据集上仍展现出良好效果。