Skill Extraction
This release is an adaptation of the Green dataset (https://github.com/acp19tag/skill-extraction-dataset). The original dataset includes annotations of relevant entities related to different skill categories such as Skill, Qualification, Experience, Occupation, and Domain. We have adapted this dataset by remapping the existing annotations into only the Skills and Occupation categories in order to improve the model’s decision process.
Description
This dataset is a collection of hard skill entities extracted from a corpus of resumes. It is designed to benchmark the differences in skill extraction performance between human annotators and automatic systems. The resource contains two types of labels:
- Human-Annotated Labels: Created during an organized student workshop at the EHL Business School. Multiple annotations per CV were collected to establish a reliable consensus for the ground truth.
- Automatic System Labels: Generated by a state-of-the-art supervised machine learning system and conversational LLM (see related paper).
Reference
If you use this dataset, please cite the following publication:
Vásquez-Rodríguez, L., Audrin, B., Michel, S., Galli, S., Rogenhofer, J., Negro Cusa, J., & Van Der Plas, L. (2025). A human perspective to ai-based candidate screening. Proceedings of the 58th Hawaii International Conference on System Sciences