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:由于转型过程,德国劳动力市场高度依赖职业培训、再培训和继续教育。为了匹配培训者和课程提供者,我们提出了一种新颖的方法,自动检测德国培训课程和广告中的教育和培训准入。我们将特别关注(a)普通学校和教育学位,离校证书,(b)职业经验,(c)前往学徒培训和(d)由德国联邦就业署提供的技能列表。该方法结合了几种方法:首先,我们提供了一种教育词汇同义词映射的方法,将不同的资格结合起来,添加不再使用的术语。其次,我们提供基于规则的匹配,以确定对职业经验或学徒培训的需求。然而,由于数据架构不兼容或非标准化要求(例如初始测试或面试),并非所有准入要求都可匹配。尽管我们可以识别出一些缺点,但所提出的方法在两个数据集(培训和再培训广告)中都提供了有前途的结果。