@inproceedings{gnehm-etal-2022-fine,
    title = "Fine-Grained Extraction and Classification of Skill Requirements in {G}erman-Speaking Job Ads",
    author = {Gnehm, Ann-sophie  and
      B{\"u}hlmann, Eva  and
      Buchs, Helen  and
      Clematide, Simon},
    editor = "Bamman, David  and
      Hovy, Dirk  and
      Jurgens, David  and
      Keith, Katherine  and
      O'Connor, Brendan  and
      Volkova, Svitlana",
    booktitle = "Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science (NLP+CSS)",
    month = nov,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.nlpcss-1.2",
    doi = "10.18653/v1/2022.nlpcss-1.2",
    pages = "14--24",
    abstract = "Monitoring the development of labor market skill requirements is an information need that is more and more approached by applying text mining methods to job advertisement data. We present an approach for fine-grained extraction and classification of skill requirements from German-speaking job advertisements. We adapt pre-trained transformer-based language models to the domain and task of computing meaningful representations of sentences or spans. By using context from job advertisements and the large ESCO domain ontology we improve our similarity-based unsupervised multi-label classification results. Our best model achieves a mean average precision of 0.969 on the skill class level.",
}