  [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.
