# Joint Extraction and Classification of Danish Competences for Job Matching

arXiv: https://arxiv.org/abs/2410.22103 · HTML: https://mlsanity.com/paper/2410.22103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [Learning Representations for Soft Skill Matching](https://mlsanity.com/paper/1807.07741/) (2018)
- [DataOps for Societal Intelligence: a Data Pipeline for Labor Market Skills Extraction and Matching](https://mlsanity.com/paper/2104.01966/) (2021)
- [Kompetencer: Fine-grained Skill Classification in Danish Job Postings via Distant Supervision and Transfer Learning](https://mlsanity.com/paper/2205.01381/) (2022)

## Built on by (most cited first, 1 of 1)

- [CourseGraph: Finding overlaps and differences in Computer Science courses across universities](https://mlsanity.com/paper/2608.05910/) (2026)

## Abstract (arXiv)

The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs can greatly promote recruiters' productivity in locating relevant candidates for job vacancies. This work presents the first model that jointly extracts and classifies competence from Danish job postings. Different from existing works on skill extraction and skill classification, our model is trained on a large volume of annotated Danish corpora and is capable of extracting a wide range of Danish competences, including skills, occupations and knowledges of different categories. More importantly, as a single BERT-like architecture for joint extraction and classification, our model is lightweight and efficient at inference. On a real-scenario job matching dataset, our model beats the state-of-the-art models in the overall performance of Danish competence extraction and classification, and saves over 50% time at inference.