Domain Adaptation for Resume Classification Using Convolutional Neural Networks
This addresses the challenge of resume classification for HR applications, but it is incremental as it applies existing domain adaptation techniques to a specific domain.
The paper tackled the problem of classifying resumes into job categories with limited labeled data by using domain adaptation from freely available job descriptions, achieving reasonable classification performance.
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.