Domain Adaptation for Resume Classification Using Convolutional Neural Networks

TL;DR

This paper introduces a domain adaptation approach using CNNs to classify resumes into job categories by leveraging large amounts of job description data, achieving reasonable accuracy with limited labeled resumes.

Contribution

It presents a novel CNN-based domain adaptation method for resume classification, effectively utilizing job description data to compensate for scarce labeled resume data.

Findings

Effective classification performance with limited resume data

Successful transfer learning from job descriptions to resumes

CNN-based domain adaptation improves accuracy

Abstract

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.