Hardware-effective Approaches for Skill Extraction in Job Offers and Resumes
| Type of publication: | Conference paper |
| Citation: | Vasquez-Rodriguez_RECSYSINHR24_2024 |
| Publication status: | Accepted |
| Booktitle: | The 4th Workshop on Recommender Systems for Human Resources, in conjunction with the 18th ACM Conference on Recommender Systems |
| Volume: | 3788 |
| Year: | 2024 |
| Month: | July |
| URL: | https://ceur-ws.org/Vol-3788/R... |
| Abstract: | Recent work on the automatic extraction of skills has mainly focused on job offers and not resumes while using state-of-the-art resource-intensive methods and considerable amounts of annotated data. However, in real-life industrial contexts, the computational resources and the annotated data available can be limited, especially for resumes. In this paper, we present our experiments that use hardware-effective methods and circumvent the need for large amounts of annotated data. We experiment with various methods that vary in hardware requirements and complexity. We evaluate these systems both on public and commercial data, using gold-standard for evaluation. We find that standalone rule-based and semantic model performance on the skill extraction task is limited and variable between job offers and resumes. However, neural models can perform competitively and be more stable, even when using small datasets, with an improvement of $\thicksim$30\%. We present our experiments using minimal hardware, mostly CPU-based with less than 8 GB of RAM for rule-based and semantic methods and using GPUs for neural models with a maximum memory usage for both CPU and GPU of 24 GB, with less than 25 minutes of training time. |
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Idiap SEM24 |
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| Added by: | [UNK] |
| Total mark: | 0 |
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