Abstract
The goal of human resource management is to select the right people to the right positions, no matter by recruitment, assessment or promotion. To achieve this goal, competency analysis is an effective way. We can obtain the employee’s competency and the position’s requirements by the analysis. The competency analysis also provide a strong intellectual support in the downstream works, such as assessing or promoting employees, or establishing employee files. The multi-label text classification model, which is proposed in this paper based on deep neural network, can successfully complete the competency analysis, and its performance is much better than the current text multi-label classification method. We also construct a multi-label classification dataset in human resource field, which is the first one focused on competency analysis, as far as we know.
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This work is supported by Big Data Research Foundation of PICC.
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Qiao, G., Wu, B., Wang, B., Zhang, B. (2019). MLCA: A Multi-label Competency Analysis Method Based on Deep Neural Network. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_59
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DOI: https://doi.org/10.1007/978-3-030-35231-8_59
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