@inproceedings{senger-etal-2025-karrierewege,
title = "{KARRIEREWEGE}: A large scale Career Path Prediction Dataset",
author = "Senger, Elena and
Campbell, Yuri and
van der Goot, Rob and
Plank, Barbara",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.46/",
pages = "533--545",
abstract = "Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce Karrierewege, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in Karrierewege+. This allows for accurate predictions from unstructured data, closely aligning with practical application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a previous benchmark and see increased performance and robustness by synthesizing the data for the free-text use cases."
}
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%0 Conference Proceedings
%T KARRIEREWEGE: A large scale Career Path Prediction Dataset
%A Senger, Elena
%A Campbell, Yuri
%A van der Goot, Rob
%A Plank, Barbara
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F senger-etal-2025-karrierewege
%X Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce Karrierewege, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in Karrierewege+. This allows for accurate predictions from unstructured data, closely aligning with practical application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a previous benchmark and see increased performance and robustness by synthesizing the data for the free-text use cases.
%U https://aclanthology.org/2025.coling-industry.46/
%P 533-545
Markdown (Informal)
[KARRIEREWEGE: A large scale Career Path Prediction Dataset](https://aclanthology.org/2025.coling-industry.46/) (Senger et al., COLING 2025)
ACL
- Elena Senger, Yuri Campbell, Rob van der Goot, and Barbara Plank. 2025. KARRIEREWEGE: A large scale Career Path Prediction Dataset. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 533–545, Abu Dhabi, UAE. Association for Computational Linguistics.