We propose a novel method for classifying resume data of job applicants into 27 different job categories using convolutional neural networks. Since resume data is costly and hard to obtain due to its sensitive nature, we use domain adaptation. In particular, we train a classifier on a large number of freely available job description snippets and then use it to classify resume data. We empirically verify a reasonable classification performance of our approach despite having only a small amount of labeled resume data available.
CITATION STYLE
Sayfullina, L., Malmi, E., Liao, Y., & Jung, A. (2018). Domain adaptation for resume classification using convolutional neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10716 LNCS, pp. 82–93). Springer Verlag. https://doi.org/10.1007/978-3-319-73013-4_8
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