Employing Natural Language Processing Techniques for Online Job Vacancies Classification

What is it about?

This paper elaborates on the automatic classification of free-text Web job vacancies on a standard taxon-omy of occupations. In achieving this, we draw on well-established approaches for extracting textual features, which subsequently are employed for training ma-chine learning algorithms. The training and evaluation of our machine learning models were performed with data extracted from online sources, pre-processed, and hand-annotated following the ISCO taxonomy. The results showed that the proposed model is very promising. The advantage is its simplicity. After its ap-plication to a relatively small and difficult to clean dataset, it achieved a good accuracy. Furthermore, in this paper we discuss how real-life applications for skill anticipation and matching could benefit from our approach

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Photo by Stephen Dawson on Unsplash

Photo by Stephen Dawson on Unsplash

Why is it important?

Because it deals with NLP problem in a simple way