Natural Language Processing Applied to Recruitment
Abstract
Information is growing exponentially, and with this, traditional personnel selection techniques have been transformed as organizations have begun to adopt and take advantage of artificial intelligence in their hiring processes. This work addresses the use of Natural Language Processing by proposing a text-processing methodology that helps determine relationships between candidate profiles and job offers. The project involves capturing data from the LinkedIn social network using Web Scraping, then cleaning, adapting, and transforming the information to utilize different Word embedding and Transformer models in Natural Language Processing. The goal is to classify the candidates most compatible with the job offers, thus generating a new set of tools to facilitate decision-making in personnel selection.
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