Annotated Job Ads with Named Entity Recognition
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 18 Oct 2023.
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5 of 11 references.
References · 5
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselinescited by 255
- 2020Revisiting Few-sample BERT Fine-tuningcited by 238
- 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128
- 2020Playing with Words at the National Library of Sweden -- Making a Swedish BERTcited by 35
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