Summary of Hierarchical Classification Of Transversal Skills in Job Ads Based on Sentence Embeddings, by Florin Leon et al.
Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings
by Florin Leon, Marius Gavrilescu, Sabina-Adriana Floria, Alina-Adriana Minea
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a framework to identify correlations between job ad requirements and transversal skill sets using a deep learning model. The approach involves collecting, preprocessing, and labeling data using ESCO taxonomy. Hierarchical classification and multi-label strategies are employed for skill identification, while augmentation techniques address data imbalance and enhance model robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand what skills employers need from job applicants. It uses a special type of artificial intelligence called deep learning to analyze job ads and find the necessary skills for each job description. The team used a big database of job ads from all over Europe, which helped them develop a more accurate model that can work with different languages. This is an important step towards helping people find jobs that match their skills. |
Keywords
* Artificial intelligence * Classification * Deep learning
