Abstract
Automatic keyword extraction has important applications in various fields such as information retrieval, text mining and automatic text summarization. Different models of keyword extraction exist in the literature. In most cases, these models are designed for English-language documents, including scientific journals, news articles, or web pages. In this work, we evaluate state-of-the-art unsupervised approaches for extracting keywords from French-language Curricula Vitae (CVs) and job offers. The goal is to use these keywords to match a candidate and a job offer as part of a job recommendation system. Our evaluation showed that statistical baselines obtain good results with an interesting processing time in an industrial context. It also allowed us to highlight, on the one hand, biases related to pre-trained word embedding models on corpora of a different nature than CVs and job offers, and on the other hand, the difficulties of annotation within the framework of job search platforms.
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Notes
- 1.
KeyphraseVectorizers: https://github.com/TimSchopf/KeyphraseVectorizers.
- 2.
French model Spacy: https://spacy.io/models/fr.
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Acknowledgement
We would like to thank Servan Cazenave, director of Inasoft, for his support for this project, his contribution on domain knowledge, and his help in annotation.
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Audeh, B., Sutter, M., Largeron, C. (2023). Comparative Study of Unsupervised Keyword Extraction Methods for Job Recommendation in an Industrial Environment. In: Nurcan, S., Opdahl, A.L., Mouratidis, H., Tsohou, A. (eds) Research Challenges in Information Science: Information Science and the Connected World. RCIS 2023. Lecture Notes in Business Information Processing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-031-33080-3_37
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