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We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as backbone.
2018
Earlier work this paper cites.
Sayfullina, L., Malmi, E., Kannala, J.: Learning representations for soft skill matching. In: Analysis of Images, Social Networks and Texts: 7th International Conference, AIST 2018, Moscow, Russia, July 5–7, 2018, Revised Selected Papers 7. pp. 141–152. Springer (2018)
2018
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2019
Earlier work this paper cites.
Bhola, A., Halder, K., Prasad, A., Kan, M.Y.: Retrieving skills from job descriptions: A language model based extreme multi-label classification framework. In: Proceedings of the 28th International Conference on Computational Linguistics. pp. 5832–5842 (2020)
2020
Earlier work this paper cites.
Chernova, M.: Occupational skills extraction with FinBERT. Master’s thesis (2020)
2020
Earlier work this paper cites.
Tamburri, D.A., Van Den Heuvel, W.J., Garriga, M.: Dataops for societal intelligence: a data pipeline for labor market skills extraction and matching. In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). pp. 391–394. IEEE (2020)
2020
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2022
Cited alongside, same era.
Gnehm, A.S., Bühlmann, E., Clematide, S.: Evaluation of transfer learning and domain adaptation for analyzing german-speaking job advertisements. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. pp. 3892–3901 (2022)
2022
Cited alongside, same era.
Green, T., Maynard, D., Lin, C.: Development of a benchmark corpus to support entity recognition in job descriptions. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. pp. 1201–1208 (2022)
2022
Cited alongside, same era.
Mashayekhi, Y., Li, N., Kang, B., Lijffijt, J., Bie, T.D.: A challenge-based survey of e-recruitment recommendation systems (2022)
2022
Cited alongside, same era.
2022
Later among the works it cites.
European Commision: European skills, competences, qualifications and occupations dataset v1.1.1 (2023), https://esco.ec.europa.eu/en/use-esco/download
2023
Closest in time.
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y.J., Madotto, A., Fung, P.: Survey of hallucination in natural language generation. ACM Computing Surveys 55
2023
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2023
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2022
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2023
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