README.md

---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- Skill Extraction
pretty_name: Skill Extraction - TECH
size_categories:
- n<1K
---
# Skill Extraction with ESCO skills - TECH subset

## Dataset Description

- **Paper:** https://arxiv.org/abs/2209.05987
- **Point of Contact:** [email protected]

## Dataset Summary

This dataset contains an extension of the `TECH` subset form the [SkillSpan](https://arxiv.org/abs/2204.12811) dataset, in which spans of skill mentions in sentences have been labeled with corresponding [ESCO](https://esco.ec.europa.eu/en/classification/skill_main) skills (ESCO v1.1.0).

This dataset is part of a three-part evaluation dataset for skill extraction:
1. [**skill-extraction-tech**](https://huggingface.co/datasets/jensjorisdecorte/skill-extraction-tech)
2. [**skill-extraction-house**](https://huggingface.co/datasets/jensjorisdecorte/skill-extraction-house)
3. [**skill-extraction-techwolf**](https://huggingface.co/datasets/jensjorisdecorte/skill-extraction-techwolf)

### Citation Information

If you use this dataset, please include the following reference:

```
@inproceedings{decorte2022design,
  articleno    = {{4}},
  author       = {{Decorte, Jens-Joris and Van Hautte, Jeroen and Deleu, Johannes and Develder, Chris and Demeester, Thomas}},
  booktitle    = {{Proceedings of the 2nd Workshop on Recommender Systems for Human Resources (RecSys-in-HR 2022)}},
  editor       = {{Kaya, Mesut and Bogers, Toine and Graus, David and Mesbah, Sepideh and Johnson, Chris and Gutiérrez, Francisco}},
  isbn         = {{9781450398565}},
  issn         = {{1613-0073}},
  language     = {{eng}},
  location     = {{Seatle, USA}},
  pages        = {{7}},
  publisher    = {{CEUR}},
  title        = {{Design of negative sampling strategies for distantly supervised skill extraction}},
  url          = {{https://ceur-ws.org/Vol-3218/RecSysHR2022-paper_4.pdf}},
  volume       = {{3218}},
  year         = {{2022}},
}
```