Análise Comparativa entre Abordagens de Aprendizado de Máquina para Classificação Automática de Currículos de Profissionais de TIC

  • Renato Santos Pereira Instituto Federal do Espírito Santo
  • Hilário Tomaz Alves de Oliveira Instituto Federal do Espirito Santo

Resumo


A triagem de currículos desempenha um papel crucial no recrutamento de talentos nas empresas. Contudo, lidar com um grande volume de currículos pode ser demorado e complexo. Com o objetivo de automatizar essa tarefa, diversos trabalhos têm explorado técnicas de processamento de linguagem natural e algoritmos de aprendizado de máquina. Nesse contexto, este trabalho apresenta uma análise comparativa de diferentes abordagens para a classificação automática de currículos de profissionais de Tecnologia da Informação e Comunicação (TIC). As abordagens investigadas incluem algoritmos tradicionais, modelos baseados em redes neurais profundas e modelos neurais de linguagem pré-treinados. Foram realizados experimentos utilizando um conjunto de 27.405 currículos, distribuídos em oito categorias relacionadas aos profissionais de TIC. Os resultados obtidos revelam que, de maneira geral, os modelos pré-treinados alcançaram os melhores desempenhos, especialmente, o modelo RoBERTa-base, que obteve resultados superiores a 93,00% em todas as medidas de avaliação utilizadas.

Palavras-chave: Triagem de currículos, Recrutamento de talentos, Processamento de linguagem natural, Algoritmos de aprendizado de máquina, Redes Neurais

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Publicado
25/09/2023
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PEREIRA, Renato Santos; OLIVEIRA, Hilário Tomaz Alves de. Análise Comparativa entre Abordagens de Aprendizado de Máquina para Classificação Automática de Currículos de Profissionais de TIC. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 359-373. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.234140.