  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.
