Annotated Job Ads with Named Entity Recognition
Felix Stollenwerk Niklas Fastlund Anna Nyqvist Joey Öhman
We have trained a named entity recognition (NER) model that screens Swedish
job ads for different kinds of useful information (e.g. skills required from a
job seeker). It was obtained by fine-tuning KB-BERT. The biggest challenge we
faced was the creation of a labelled dataset, which required manual annotation.
This paper gives an overview of the methods we employed to make the annotation
process more efficient and to ensure high quality data. We also report on the
performance of the resulting model.