2022/04/11 by David Beauchemin, Julien Laumônier, Beauchemin, David +5 · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · #AI and HR Technologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scheduling and Timetabling Solutions #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2204.05208
openalex publication_date 2022/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Understanding the evolution of job requirements is becoming more important for workers, companies and public organizations to follow the fast transformation of the employment market. Fortunately, recent natural language processing (NLP) approaches allow for the development of methods to automatically extract information from job ads and recognize skills more precisely. However, these efficient approaches need a large amount of annotated data from the studied domain which is difficult to access, mainly due to intellectual property. This article proposes a new public dataset, FIJO, containing insurance job offers, including many soft skill annotations. To understand the potential of this dataset, we detail some characteristics and some limitations. Then, we present the results of skill detection algorithms using a named entity recognition approach and show that transformers-based models have good token-wise performances on this dataset. Lastly, we analyze some errors made by our best model to emphasize the difficulties that may arise when applying NLP approaches.