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.
@article{arxiv.1707.05576,
title = {Domain Adaptation for Resume Classification Using Convolutional Neural Networks},
author = {Luiza Sayfullina and Eric Malmi and Yiping Liao and Alex Jung},
journal= {arXiv preprint arXiv:1707.05576},
year = {2017}
}
Comments
To be published in AIST proceedings: Springer's Lecture Notes in Computer Science (LNCS) series