[Abridged Abstract]
Recent technological advances underscore labor market dynamics, yielding
significant consequences for employment prospects and increasing job vacancy
data across platforms and languages. Aggregating such data holds potential for
valuable insights into labor market demands, new skills emergence, and
facilitating job matching for various stakeholders. However, despite prevalent
insights in the private sector, transparent language technology systems and
data for this domain are lacking. This thesis investigates Natural Language
Processing (NLP) technology for extracting relevant information from job
descriptions, identifying challenges including scarcity of training data, lack
of standardized annotation guidelines, and shortage of effective extraction
methods from job ads. We frame the problem, obtaining annotated data, and
introducing extraction methodologies. Our contributions include job description
datasets, a de-identification dataset, and a novel active learning algorithm
for efficient model training. We propose skill extraction using weak
supervision, a taxonomy-aware pre-training methodology adapting multilingual
language models to the job market domain, and a retrieval-augmented model
leveraging multiple skill extraction datasets to enhance overall performance.
Finally, we ground extracted information within a designated taxonomy.

利用自然语言处理技术从职位描述中提取相关信息，包括解决训练数据的稀缺性、标注指南的缺乏以及从职位广告中提取有效信息的不足等挑战，最终通过引入抽取方法和多个数据集相辅相成的检索增强模型来提高整体性能，并将提取到的信息置于特定分类体系中。